{
  "nbformat": 4,
  "nbformat_minor": 2,
  "metadata": {
    "colab": {
      "name": "ResNet.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP4ZVwKNABr2g499n+4/ztC",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python385jvsc74a57bd06d46af94c2bbce495f1e668725902fa517c90b1782bcfe2fce0dd9868df553d3",
      "display_name": "Python 3.8.5 64-bit ('base': conda)"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.5"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<a href=\"https://colab.research.google.com/github/Taehee-K/Brain-Tumor-Classification/blob/main/code/ResNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ],
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "import os\r\n",
        "path = os.path.dirname(os.path.abspath(__file__))\r\n",
        "os.chdir(path)"
      ],
      "outputs": [],
      "metadata": {
        "id": "mbF-t5mNPJP6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Split Data"
      ],
      "metadata": {
        "id": "0mlQ8IxbPV6x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Train, Validation, Test 데이터 폴더 나누기"
      ],
      "metadata": {
        "id": "nfurc7XoQROn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "import shutil\r\n",
        " \r\n",
        "original_dataset_dir = '../BrainTumorData'   \r\n",
        "classes_list = os.listdir(original_dataset_dir) \r\n",
        " \r\n",
        "base_dir = '../splitted'                           # train-validation 데이터 나누어 저장\r\n",
        "os.mkdir(base_dir)\r\n",
        " \r\n",
        "train_dir = os.path.join(base_dir, 'train')       # train data\r\n",
        "os.mkdir(train_dir)\r\n",
        "test_dir = os.path.join(base_dir, 'val')          # test data\r\n",
        "os.mkdir(test_dir)\r\n",
        "\r\n",
        "for cls in classes_list:     \r\n",
        "    os.mkdir(os.path.join(train_dir, cls))\r\n",
        "    os.mkdir(os.path.join(test_dir, cls))"
      ],
      "outputs": [],
      "metadata": {
        "id": "vdgUY4_vPVS_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Train:Test 8:2 로 데이터 분할\n",
        "* 각 클래스 별 데이터 수 확인"
      ],
      "metadata": {
        "id": "ncEjsRz_QVoR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "import math\r\n",
        " \r\n",
        "for cls in classes_list:\r\n",
        "    path = os.path.join(original_dataset_dir, cls)\r\n",
        "    fnames = os.listdir(path)\r\n",
        " \r\n",
        "    train_size = math.floor(len(fnames) * 0.8)\r\n",
        "    test_size = math.floor(len(fnames) * 0.2)\r\n",
        "    \r\n",
        "    train_fnames = fnames[:train_size]\r\n",
        "    print(\"Train size(\",cls,\"): \", len(train_fnames))\r\n",
        "    for fname in train_fnames:\r\n",
        "        src = os.path.join(path, fname)\r\n",
        "        dst = os.path.join(os.path.join(train_dir, cls), fname)\r\n",
        "        shutil.copyfile(src, dst)\r\n",
        "        \r\n",
        "    test_fnames = fnames[train_size:]\r\n",
        "    print(\"Test size(\",cls,\"): \", len(test_fnames))\r\n",
        "    for fname in test_fnames:\r\n",
        "        src = os.path.join(path, fname)\r\n",
        "        dst = os.path.join(os.path.join(test_dir, cls), fname)\r\n",
        "        shutil.copyfile(src, dst)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train size( Brain Tumor ):  2010\n",
            "Test size( Brain Tumor ):  503\n",
            "Train size( Healthy ):  1669\n",
            "Test size( Healthy ):  418\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8aPlJ4qIQKyS",
        "outputId": "2088d200-e874-4730-99c6-ac87cec7ea7b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Modules"
      ],
      "metadata": {
        "id": "gY3Ec2iGQoQh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "source": [
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "# Load Data\r\n",
        "import torchvision.transforms as transforms\r\n",
        "from torchvision.datasets import ImageFolder \r\n",
        "from torch.utils.data import DataLoader\r\n",
        "\r\n",
        "# Pytorch --> MLP, CNN\r\n",
        "import torch\r\n",
        "import torch.nn as nn \r\n",
        "import torch.nn.functional as F\r\n",
        "import torch.optim as optim\r\n",
        "\r\n",
        "from torchsummary import summary"
      ],
      "outputs": [],
      "metadata": {
        "id": "4xWouD7KQnuo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "source": [
        "if torch.cuda.is_available():\r\n",
        "  DEVICE = torch.device('cuda')\r\n",
        "else:\r\n",
        "  DEVICE = torch.device('cpu')\r\n",
        "print(DEVICE)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cpu\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "21eJSOYPQw5f",
        "outputId": "c340868c-62b3-4b30-bea8-3d76f185782c"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "source": [
        "BATCH_SIZE = 64\r\n",
        "EPOCH = 30"
      ],
      "outputs": [],
      "metadata": {
        "id": "zIDQtchdQxGe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "source": [
        "original_dataset_dir = '../BrainTumorData'   \r\n",
        "classes_list = os.listdir(original_dataset_dir) \r\n",
        "n_classes = len(classes_list)\r\n",
        "\r\n",
        "print(classes_list)           # 분류해야 할 클래스들\r\n",
        "print(n_classes)      # 클래스 수 2개"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Brain Tumor', 'Healthy']\n",
            "2\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QkMRfTeJQ2j-",
        "outputId": "4bc8197f-4227-4bf8-a754-fd61a2dc6a23"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Data"
      ],
      "metadata": {
        "id": "QAqMDgOiUvSK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "source": [
        "transform_base = transforms.Compose([transforms.Resize((227,227)),\r\n",
        "                                     transforms.ToTensor(),\r\n",
        "                                     transforms.Grayscale(num_output_channels=1)]) \r\n",
        "train_dataset = ImageFolder(root='../splitted/train', transform=transform_base)\r\n",
        "test_dataset = ImageFolder(root='../splitted/val', transform=transform_base)"
      ],
      "outputs": [],
      "metadata": {
        "id": "hwA82Zz6RKq3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "source": [
        "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)\r\n",
        "test_loader = torch.utils.data.DataLoader(test_dataset,batch_size=BATCH_SIZE, shuffle=True, num_workers=4)"
      ],
      "outputs": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvRneP0vRNsL",
        "outputId": "e6a95a30-fce7-4e7c-cac0-039f8700967e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 데이터 개수, 형태 확인"
      ],
      "metadata": {
        "id": "Lo4OE5Wdb3xI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "source": [
        "for (X_train, y_train) in train_loader:\r\n",
        "    print('X_train:', X_train.size(),'type:', X_train.type())\r\n",
        "    print('y_train:', y_train.size(),'type:', y_train.type())\r\n",
        "    break"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train: torch.Size([64, 1, 227, 227]) type: torch.FloatTensor\n",
            "y_train: torch.Size([64]) type: torch.LongTensor\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqysFnG7bxZs",
        "outputId": "2334ad90-9dca-476d-f66c-844ab9f72f59"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 데이터 사진 확인하기"
      ],
      "metadata": {
        "id": "Hb9ryNtsTf8W"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "pltsize = 2\r\n",
        "plt.figure(figsize=(10 * pltsize, pltsize))\r\n",
        "for i in range(10):\r\n",
        "    plt.subplot(1, 10, i + 1)\r\n",
        "    plt.axis('off')\r\n",
        "    plt.imshow(X_train[i, :, :, :].numpy().reshape(227, 227), cmap = \"gray_r\")\r\n",
        "    plt.title('Class: ' + str(y_train[i].item()))"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x144 with 10 Axes>"
            ],
            "image/png": 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y0Rh8e0RSTmT4xbksbmLi//nvlvse3UvcsPj28PcUn6vSPcTnKkculIO4+fHPUS5PmmvvIydqVqqtsFrC5UE/4w11cf0r19/3gihDUTF6lERNsViEVquttK5uOKJGnFv8fCIFliezSUkkgkWM1OIh9pv4ot/l83mo1eqyxsUakDfriqi5n3G6mmuQjsLXUuDTWmhvJTnzRiVvsPDGEkVX8N5o8b4rybDa8q0mUVO8DwHcS++jPieSJhQK4erVq3j33Xdx6dIlTExMIBaLlfSjSqWC2WxGZ2cnDhw4gMOHD8NkMjFDQKFQIBQKYWJiAtFoFD6fD2fOnMFzzz3HImxSqRQAoL6+HjabDXa7HVqtFnq9HlqtFtlsFnNzc7h9+zbOnTuH/v5+BINBNt81Gg2amprw5JNP4tixYzhy5AhaWlqg0+lKDMByNXoeUrYbzjjkdRGaI+UM8JXWtXvtYfz6SfOS7s3vjTSv+euVs08ew/palRukUqki9R2/5/P9DZQ6dEV9jCcgKumjhFwud5eDmSeEeH2mEulBeyoRK+IaSusBtVWtVt91XboW35Zy9hEPPqVLJOH5tUXsTwKv9yoUCmi12g03F+mZcrkcAoEAJicnMTQ0hOHhYYyPjzN52Gw2OJ1OWK1WZs/r9XoWPUPXooiaaDTK+jSVSsHr9SIYDGJ+fh5+v5+RPE1NTdi3bx/27t2L5uZmGI3Gu1JHH0cfPDKihoyKB3mYcobealEoFJBOp+Hz+TA7Owuv14toNFoSwsYrqMSmajQaaLVa1NTUoLa2FrW1tTAYDHcpOiLuZbTQd8Tnq/R+ued+HERNPp8vigsn304xPJbay/97L6WOno9n0tVqNfL5PILBIBKJBGNG4/E4M1CTySQymQwSiQTUanXJS6PRQKVSMZY0HA4zebrdbjidTsTjcSSTSSSTSajVaigUCjQ0NKCurg6tra1MGSKlhUKledJuJeKh3EK5mjBV+p1Sqayqccgz/qIcy5EbIiFTjt0vZ6iLBqJ4Hdoo+EJttMmkUikkEgkkEgkmm1Qqxe5Dm5TRaGS5+CaTibHb4mZbblOjtvFKFk888uOXSENxc1vtOvQoPYdrQdSI45mUeVJaRPBjYzXg12He8yW2717XWC02O1FDchHlI5IqtL7RmKcQYPqMlwOPcoQNr6Dyygsv28dM0LDmVutC64WoEY0CWhP4CItIJMJqn1D9E1pLKbqUnBQ6nQ4Gg4EptOR5lCSJRe6Va8NK7asm1oqouddXyWgPhUK4ePEiTp8+jXfeeQdutxuZTKZkP9JoNGhra8NTTz2FZ555Bvv370ckEmFeXwAwm83QarWYnJzExMQEstksIpEIrl69CrVaDYfDAZfLhVwuh1QqBYvFAoVCAavVyogatVqNnTt3wmw2w2QyQafTwe1246OPPsKZM2cwNDSERCLB9j6LxYI9e/bgD/7gD/DKK6+gp6cHRqMRmUymJLqD8DCy3UgRNeI6yq9nBJGood+J4MkX/vri/8ulrfDfE+2oSqTDY0iLqlpEjbgnEcqNO1F3A+7oqLwzme8PvviwGDAg6h38msrbNuUcSeJv6HNe9yynX4rPQDYnfUeU+2rmWzmyh3fCiH240Yga6otUKoXZ2VmcO3cOvb29iMfjLPqwoaEBNpsNGo2GjQm9Xl9iZ9JYoLRTvV7P7Hp+nSZCL5PJIBaLIRqNIhAIYGpqCh6PB+FwGN3d3Th+/Dh27twJu91eEkn8mFE9ooY6qdICcj/KuEiUiP+n+0UiEUxOTmJqagqJRKKEMUskEohEIshms9BqtawNlJtmsVig1WrZtZVKJWw2GxoaGtDU1ASj0Vg2VLzcQn4vrGQoie/fYzBUbZTkcjkWjQGgxHAVWWlxoRLbzBpXhhSgkE/y4MTjcczPz8Pr9UKSJMzNzcHj8SAWi8Fut6O5uRlWqxWFQgFqtZqFDZOCo9FooNFoGKFTKBRY9JTNZkMqlYJOp0Mmk0EgEGCkgMfjgcfjgclkQk9PD/NU0ekJknQnb1yUsUhkVXr21Si2y4t21YxD6mPeGwSUJ47EecQrmZWID35TEXNr+TlBGwaRRul0GqFQCG63G5OTk0xZpblHRQ9pnhHrnUqlEA6HEY/HoVQqUVNTg46ODrS3t7MQ7nIkDIF/Dn7jFmXDE0rlSDge5d6rItkGCGsqRQauVHhvtWRMuTkpflZOSeU9STxpR1CpVCVeLl7pJYjECMmKT+kieZUjQFdq+72wmYkaUtBozaY0iXQ6zYxw0QATiVOeoOXXD+COXMqNqXg8jnQ6DYViKc+b9lBa4/k98zFiwxI15fpYoVCU7F9E0ExMTMDtdmN2dhYLCwtIJBIsapjy80mulPZrsViYsmowGGC329HQ0ICGhgZ2ipDJZCpZC/i95GH1udU+fyQSQXNz85rPRbEoaDabxfDwMP7jP/4Dv/3tbzE7O8v6muaJWq1Gd3c3vvSlL+HYsWNIp9PIZDKQJAmZTIbpMnRtjUaDhYUFRuLEYjEMDg6iUCjA6XTCZrOxPVShUCCVSqGhoYFFEiuVSnR1dTFnhtFoRC6XY3L88MMPcfLkSQwMDLB2AEtpUYcPH8Yf//Ef4+WXX0ZjY2OJc4qPHHhQSNWd+I9sTaW9qlwmQCW9l4zBlVLGyu1Zou7MEw3ifiwa+WIkCf2f5PYIURU5JpPJkqjvSiQYr8vxjkeyR3j9g+/Hcg4j0V6jPuWjD4HSSCUaB+TIENdBvm4N/zz8+CEyhpcvn7akUCzVeyObSBxH5d4D7kRN3kuX4/WnjUTUFItLGRVzc3M4e/YsLl26BKPRiF27dqGpqYnpbmQzUJqTWGMmmUzC6/UimUyy7+l0OtTU1ECj0SAcDjNdUKfTQa/Xl+hA6XSa2ZVerxejo6MYGRlBbW0tvvjFL2LXrl2w2WxrUeuwekQNdTaAuxi+lSZRxZsIxgO9crkcFhcXMTo6ivn5eQBLEy4ej8Pn8wEAWlpaUFdXxzYu/ujCSCSC0dFRtlEWi0VYrVa4XC5mCCoUCtTU1KCtrQ01NTV3eSHFdld6DpFwoucq9/w0uSvUUWCXXOnD+0E2my3yiyG/oIhto7/5xVI0yvjfUOoSbTRarRbpdBpzc3Nwu90s4sloNCKbzcJgMKCmpgZ1dXXQ6XTIZrMIBoOQJIlFXkiSBJPJxApEZbNZZDIZRg7EYjEAQDAYRCaTgc1mQzabhcViYTJOp9OYnJzE5OQk0uk02tra0N3dDafTySI5eOaeFt9sNsvIPL5mQLmFstLffP9UM6KmWCyy9vHehXIpfeJY5NOd+PfFCBV+/onv8V4OAMhkMlhYWMCNGzfw+eefIxaLob6+Hh0dHWhsbITL5YLVai1hwgGU9GsymUQkEsHCwgLcbjfm5uagUChYKDkpsryywm/IYqQMPTsvA5HsqRSiKnpUgKXNV6PRPFKihsZ8xR/cgyRd9Y05uVJ6Bf2f5EH9SJtYOBxGJpNhRkihUIBOp4NGo2EeXiJU+TBUAExRoTbzJKh4v3LPtFpCSnxGvi3iJVfZVffCY/E4iUoepWRQBCntvdQfiUQCgUAAPp8PPp8PgUAAoVAIkiThySefRHd3NwYHB9Hb28tSTcmg37JlCzo7O+FwOEoUT2DJGUL7J5EBBJGweVjjbxVYc6JmteRMOV2IV/Kz2SzC4TCTCUVd0DpIcw8A8w6SwqrRaBg5QPsC7cE0TqiAbXNzM/bu3Ysnn3wS27dvZ2HkZBSIOs/94H6+T+M4HA6vKVHDE830r8/nw4cffojXXnsNvb29CIVCAO48n1arxZ49e/DNb34Te/fuRaFQwOzsLGZmZmCz2dDc3Ayz2czq5gUCAczPz0OlUqG+vh4GgwGxWIyF4E9OTpash5RCpdFo4HA4WM0FIopo30omk0w3Ia9zNpvFxx9/jLfeegtTU1PIZDKsr+12O1555RV8+9vfxhNPPAGr1QoALFLuHjroXf3GYyNE1PBzAqh8TDRQSibQb0j35MkHKvhMUUo6nY71ZbFYhE6nu4sUIsOdr53It4EHvzfS+n+vo+UfElWRYyqVuku3oX95HVV01hDEVCj6LU/wVNLtaH7wcqL3xTbx7SkH/h50Ld4e4o8eJz2c/uVLPojt4/8Wjy/n94ZyRBe1h3eg8oTTo9RRq3LB5fEcCoXw2Wef4cKFC9DpdNixYwfq6+thNpthNBrZXCICL5PJMIcUcMc2JT2Iigwnk0loNBpYrVbk83lkMhmEQiEEg8GSvZB0F5qP5ByRpKVaZOPj45icnIRarWaRkiaT6XESNtUjaoA7SuRKxAWw+pAvfnDm83mEQiH09/djYmICwFJ1Z7/fD4PBgLa2NjQ2NkKr1SKXyyEWi0Gr1cLpdLJJMD09jXg8zqI0isUiYrEYPB4PZmZmEA6HUVdXh8bGRphMJhQKBTQ0NGDr1q0loU/3Mhbu5/nE/rmH8Ks28TKZTJF/FjH8U2yfuLDQe2JfUE0gfjFdXFyEx+NBIpEAsKTIE0tqsVig0+kQj8fZomYymaBWq1EoFJiBRalP1Ec0ycn7GAwGEQgEkE6nWWgcGSmSJMHhcLCJr1AosLi4iEAggGw2yyKoHA4HampqWCoUT4BU2rwJ5dJDePDffRQRNdT//CZCxhu/yYsbQblNkpe/uEGKmyDNrXw+j4WFBVy7dg3Xr18HAOzYsQPd3d1wuVxMJplMBouLiyz1kELziWyNx+OIxWIwm82w2+3I5XKMtLlx4wZSqRS2bt2KvXv3wm63s7bwGxZtkPxmThE0/ObOF3fjv8vPQTHKiL6/UYmacmsXPwZIOaCIRb/fD5/PB7fbjXA4jEgkglgsBofDwcgaGifUZrvdDpPJBJfLxY6Tra2tZamGdE/x/uX+v5r23+v5NxpRU07xJOOCJyLpO/y6S8p7KBRCX18fbty4wQx2PrXX4/FgcnISr7zyCt566y0AgE6nw9zcHDQaDbq6upihuHPnThw4cAD19fVMmaH5QCHG9D7vFOEL8m0E7y/w+Iga6pdcLsf2M5/Ph5mZGXz22Wc4ffo0bt26hWw2C6fTiZaWFnR0dDDijMgxPi1YpVIhHo+zFGHgjnySySSi0Si8Xi9u3ryJmzdvwufzweVy4eDBg2hra8PWrVvR2dmJhoaGEgL894GooXWd9rLBwUH85je/wW9+8xtWG4HfC5qamvCNb3wDr776KmKxGEKhECKRCIxGIxoaGmAymZDP5zE+Po7x8XEWYZrJZODz+aBSqdDQ0ICuri40NTWhpqYGWq0WwWAQwWAQ6XSa1b4hsoYKZOp0OhbWT+uCz+dDKBRCPp/H3NwcLBYLdu3ahUwmg3//93/He++9h1gsxohWrVaLvXv34nvf+x5efvll1NbWlhAA5VDOUOQNZaCqug1QpTWVdzTxZAdQWvRZHLcUjURGYCgUQi6Xg8fjwcLCAotkSyQSyGazrOAzAOj1etTW1rI0Cz46o1gswmQywWq1YsuWLaipqWH1Mkm/FSNYCSIxIb6qiKpF1PD7GLVdTD0SHTYEfs8TnYK83iJGIhHRKRJtdC8+mppPT+J1R/puuchi8bqi/kzvibXD+GcQiRs+qqicDVluPIh6AF9MWKfTrbu5yC627EiYnp7GO++8A6/Xi927d6OlpQUGg4FxCS6Xi9UAovmWy+VYxDnZPuQc5Oc12YaTk5MYHh6G1+uF1+tFJBJh/a/T6WAymdDQ0IADBw6goaEBsVgMs7OzUKvV2L59O/L5PAKBAEZGRtDf34/W1lYcP34cnZ2dZVNH6fmqOB+rS9SQISZOwHJMKbtZBcMBuLMohcNhDAwMYGxsjDFjZCxs27YNFosFkUgEXq8X2WwWGo0GgUAA+Xwe7e3tMBgMyGaz8Pl8CAaDrKhsOp2GxWJhxYgSiQQGBgbQ398PnU6HrVu3srApl8uFLVu2wGw2r5ietNLz8s8nCpI3qldA1SSfTqdLiBpxoeQXVWrzSosIgZjKZDIJn8+HxcVFxONxVsSZiDTKs9dqtazStslkYr+3Wq13GW38fcmrQQpobW0tM0gkaakYXywWw/DwMAKBAOrr69HQ0ACNRoNCocDSqAKBALuuwWBAXV0d807SBk2KMC2CFMosRp6IqIf76PEAACAASURBVDSPqliNvUiLM0++AKUKCL+5iW0jwkUko/gFn1dQ6X3eWxGNRnH9+nVcunQJsViMsc4GgwHRaBSRSAThcJiFZxOjbTQa0dzczAzAbDaLQCCA4eFhFItFNDY2wmg0IplMQqFQwGQyIRqN4vPPP4fP58PRo0fR3d1dUhOH2pXNZu9aRMWNlfpBVA7EuSumSS0rARuSqBHB/y6ZTLINaXZ2FrOzs0gkEoxMpaKWTqeTGYNOpxPJZBLBYJCROKlUCtFolNWhApZqM3R0dGD79u1oampipA1P+PEex0rP9PtE1PCKJA+a27lcruSVSCQwOzuL4eFhXLhwAblcDs899xycTicbt7lcjhkPp06dwuLiIpRKJb7yla9Ap9Ph1q1buH79Ol599VWYzWb4fD4MDQ1hfn4ee/fuxdNPP422tjYWrSqSvvwawnv3ZKLm7u9RP5Gz6OrVq3jrrbdw7do1WCwWPPPMM3j++eexfft2WK1WVreKivADYLX5AoEAAoEArFYr6urqWFqvUqlEPB6HSqVieg6RCDMzMzhz5gzOnj2LeDyOxsZGPPvsszh06BC2bdvGIjg2M1HDR9AUi0vHX/f29uIXv/gFzp49y8LlaY8wGAx44okn8P3vfx+7du3C7Owsixq12+3IZDKYn5/HxMQEFhcXkUgkMDo6yoieTCbDdBuNRoNMJoOGhgY88cQTrIYeAESjUcRiMczPz2NmZgahUAjhcBjZbBZmsxnNzc3QarVIJBJobW2Fy+WC0+mExWKBz+fD1NQU/H4/9Ho92tvbce3aNfzrv/4rJiYmmPFINXW+853v4Ktf/SpaWlpY8U1eL+DlBOCuvZFfA9bjSTO8jHldicjlciQH6ZfBYBCxWAxerxcTExOYm5vDxMQEFhYWmEwSiQRUKhWrv0f2gclkYuQ1kTV0cikdqb5r1y709PSgp6eHOTYoLaOcTsKPVZEwECODHhJVuVAikSgCd9rPO7wIvF3B7yP898UoXNFOEb9HhKNI+tC9i8ViyfHOJB8+qp6PTuUJF77dJF+xZht9V5wf/Hs8UVNpfxQzOcRi06INSZAkCXq9fk3nohjhzq8b8Xgc/f39OHfuHIxGI7q7u9Hc3MwyIPx+P9MdM5kM2/N4p7QoT61Wi3w+z9KfjEYjvF4v+vr6EI1Gmf5DBysApfOqoaEBPT092LJlC4xGIywWC6tdCwB+vx9zc3O4fv065ufn8corr+Dw4cMssofaxdtL5DCppNPyY77c58uoPlFDBgbvvabPRAJHhOi9z2QymJ6exq1bt5jhHQ6H0d7ejo6ODmSzWXi9XhbKbTab0djYCJVKhUgkgmAwCLfbjWQyyYiC+vp6OBwO6HQ6AEsh3BSJoVarYbPZkM/nMTExgZs3bwJYigygEKzdu3fD5XKVhCzyHV2JGa7Up/z7FMmxAqpK1PCLABnhy5stgLsXCXGylTMiKPWFjqrUaDTo7OyEVqtlKSzkFaKcQ5Elp7pCtGiS5x4AI+rS6TTzQgBLUTrkAeHJhnA4DAAsZU6tVqOpqQlKpRLBYBDJZBIOhwMmk4mdOBWNRmEymWC325myyissIoPP9yETlEB28R6SR0XUiOw6D749vNdBBPWf+DnvTSBZ5fN5uN1unD9/Hn19fdizZw8OHTqE1tZWSJKEhYUF5uGlUEW9Xl+SN18sFlnaGRFttGlSH/v9fszOzsJut6Ourg4ajQZjY2O4ffs2urq6sG/fPjZmaSMkwk8k+MqFH9Nn/PtE5pST3/Im/siJGhErrSerUdDEeUbPH41GMTMzg88//xxTU1NQqVSoq6uD3W5HS0sLGhsbWYqFUqlENptlkU/8yT9UsJRILYpoCwaDmJ2dZWtxU1MTduzYwUh2Xql5UKzUHxuNqAHuKCLlPDP8+AwEAujv78eVK1fYCQYUTrx7924cO3aMzQ0iPCkMf2RkBOfOnUN7ezuOHTsGvV6PSCSCN954A/v27cOBAweQyWQwPj6OTz/9FNPT0zCZTOjq6sL+/fuxc+dO1NXVMaOCH1O0llIx1EdciG/dEjXi+sMX/KW9ZmhoCOfOncPHH3+M6elpvPrqq/ja176GmpoaFi3h9/sxNTWFUCgEnU4Hu93OxnNjYyPUajUSiQRmZmawuLjI1lKn04kdO3Ygl8uxKIC6ujp2iALVBvvoo4/w9ttvw+PxYNeuXXjppZfwxBNPoKOjgzmnaEzyek+lZ14t1oqo4ddCIvbHxsbw2muv4c0338TY2FhJMXe1Wo3GxkZ89atfxTe+8Q1kMhmkUilWByiTyeDKlSsYGBjA5OQkI6ttNhv7Pa2fWq0W8XgcqVSKnVrZ09ODF154AYcOHUImk8H58+cxPz+PZDKJkZERRtKQM1Sr1aK1tZU5qqieX0NDA/bv34+Ojg4AwMjICLxeLzo7O5HL5fDP//zP+Oijj0oKSNvtdhw8eBB/9md/hmeffRa1tbVsTab6HCul5ZBeUSxWtf4eUIU1lSdpyu199Df9S2NhcXER8/PzGBkZwdjYGNxuN4aGhrCwsIBisYja2lrU1NRAqVRCo9EgmUwiFovB7XazGpnL/QFgyeNP+iql6kciESgUSyfYdHd3Y+/eveju7sbu3bvR3NzMiFZe9xb3NdEAFJ/tIVC1iBrSJXmSgx8zom0hEjokF14n5Z0B/DXIUV9Oz83n8yytnpzIfr8fsVgMer2eRbCRjq/VakuuT3OX2ko12lQqFUwmE5xOJ+rr62E0GsvKQLSd+GfknfS8HlROT+efTewnzpH4WFKfyuknfNt5YoTIsWg0it7eXvT19aGtrQ09PT1oaGiAUqlktWXn5uYQj8dL7sPr38CSPFOpFItCXC5JgGJxKTrVbDbD7XZjZmYGTqcTSqUS4XCYkeTURtrXFAoFjEYjWltbYbFY0NzcDJfLxeqZko0yPz+P4eFh9Pf3o729HS+++CJcLldJf5AM70XU8L9ZoS+rS9QAYKHwYvikmF7BN7oc0ZFMJtHf34+BgQHk83nMz8+jrq4Oe/fuRS6Xw+joKJLJJEufoKMP6fo0MGZmZuDz+aDX6xljB4Cxp3q9noWlFgoFVi2f8rV7e3tx69YtOBwOtLa2wmQyYcuWLdi5cydTUMUwRZGMqkROiUzrKnLeqjbxCoVCkXL6SJHmPS00eMt5G6jNPLsLgG0+i4uLmJqaQktLC+x2O/M60MlLJpMJwJ0C1GSY0kYXj8eh0WjYZ/Q5GVypVAoDAwNoa2vD3r17WZHifH7pyG+SpcFgwNDQEOrq6vDUU0/BYDCgWCwy44EKDYdCIRYFpFarWR2H1tZWOJ1Olo9I6VhibikTjrCJlMtPBVDNBbTILzSi16Ic0cCPOfoOybHc+BPnLO8ZpKNBM5kMDhw4gL179zLFlBRZAMwLTAW7CoWl+kRUq4aUQTq9jaLy6EU1MkhZ1Wq1sFqtiEQiOH36NPR6PV588UWYzea7nptkUalgqtgn4nui54d+/zgiakQ8CFHD9wPJmhRIOuL17NmzmJ+fh8vlws6dO5lnDwAL6fb7/UilUkzBJ9lRHrBOp4Pf7wcARrLSi1LUqEjb8PAwxsbGYDAY8PTTT2Pr1q0lpNqD5P5uNqKmXA0FmueFQgHhcBhXr17F5cuXsbCwgFwuxwqud3R0IBAIwO/34/jx48wpUSgU2HxLJpOYm5vDuXPn0NLSgkOHDqGurg4AcOXKFbjdbjz//POw2+0YGBjAxYsX4ff7EYlEYDAYUCgUYLPZYDab0dLSgj179mDHjh2w2+0A7hyTyq+Zj5Cs2RBEjRiRSqncv/71r/G73/0Oer0eP/rRj3Ds2DGWTx8OhzE2Ngafz8eMA3KA0PrY0tKC559/HnNzczh16hQjqcmTT2HkVD9DkiQ4nU4cPHiQtYNSu0+dOoV/+7d/g1KpxHPPPYeXX34Z+/btg8ViYYrqRidqyHCnfS8ajeL999/HT37yE9y4cQPxeLwkfcJoNOJLX/oS/vIv/xIOhwOTk5PQarWoq6tDJBLBzZs3MT4+jps3bzKHktVqRSAQYJGg5JCSpKW6B4lEAnq9HjU1NUilUkgmk3juuefw6quvolgsore3F5988gkWFxehUqlYDT6SUzwehyTdOdGNUqXoRExay/ft28eiG1UqFWpra/Gzn/0Mb775ZokhRA6sP/mTP8Gf/umforOzk+nQq6mFQnrBeiFq+D2dj0AByh93TftiMpnEzMwM+vr6cO3aNXz++eeYnp7G/v37sXv3bhw8eBBNTU1Mz6FxROsdee7T6XSJLkaOKpqz4XAYHo8HQ0NDuHr1Kvr6+hAKhaDX63HkyBE8++yzePLJJ9HS0sKOCAbujqQQU894ouYh19qqR9SUaw/tcWIauvhcwN2RpnxfiHYYT1yQM352drakzlcikYDb7YbFYmHzOhaLsflK9iA5lru7uxGPxxGJRFg0G+mVVK5Br9ejtbUVra2tqK+vL4n2Fu1A3kAnefERxnz/8GOJr2UjOlb5vXa9FBOmdmazWXg8Hpw9exZzc3OsNILD4WC2H+n+FJlNv+dtLXrOSCSC6elp+Hw+duohzUci1NxuN3NOAXfX4aL+pz0SAIsitdvt6O7uZnY/RdhYLBZEo1GMjo7i0qVLyOfz+PKXv4ydO3eydT6bzd6VdfEQqD5RQxEP/OLOdzQ/uEpuKt2pch4Oh5l3NxQKwefz4ZlnnkFjYyM7QUalUsFut6OpqemuY7iBO4txOp3G0NAQY6jJICTvBh1dmUql2P1p4yQvcSAQwLlz5xAOh7FlyxY4HA40NDRg9+7dqK2tvacweGVN/JuUbz5qY6VL3esLq0UulyuS143P/yTmWJQVv9HxzwEs5cFHo1FMTU2x4z8pZDiZTMJms8HhcECtVsNgMLDfZTIZdiwanXCRy+UwPz+PaDTKDAKaWJTOkk6ncebMGeh0OrzwwgssJY3GQDweZ9+liUnFTSmiClhSTij8MRqNYnZ2FtlslhEzNH4olJyYcqC0mns5+fKLKr8AF4tVDQ8u0vgRNwOeaScihPcoLf+4ZG6KBI9IeFDB31gshk8//RTnzp2D0+nEH/7hH6K9vZ2lVFDediqVglKpxOLiIvMekfGgUqnYwkeh4BMTEwgEAiUFLbPZLOx2O+x2O6vJQJ5FjUaDaDSKN954A0ajEa+88gq7Fik2/AYmKjCkxIljnP+bNkXxlKONFFHDPxuNw0wmg6tXr+LcuXOor6/H0aNHUVdXh2QyiUwmg3A4jHQ6zerUkAef1oypqSns3LkTra2tCAaDuHbtGvP0m0wmhMNhuN1u5PN5dHZ2wmazwWq1suMVQ6EQrl+/jpGREfT09OCpp56C2Wx+YAVzsxA1JCd+feHlRkf5Xr58GQrF0tG7lLJGp6l1dXUhk8ngwoULeOaZZ+B0Otlph1THhLyKZ8+eRWtrKw4cOMBSP71eLwYHB9HS0gKXy4Xh4WFcvnyZ1cEIhUJs3vK1MhoaGvDiiy9ix44dzAvPK4/iHl1FbAiihl93FhcXcfPmTfzud7/Db3/7W9hsNvzwhz/E4cOHkUgkmEGRSqWwsLCAQqHAPPKJRIIVWCQP/Ve/+lUoFAr853/+J9NlyMDz+/0snaJQKLAjT48cOQKr1cpkKkkSGhsbce3aNfzDP/wDAoEAXnjhBXzta1/DoUOHWNHZSvoA/8yrxeMmaoiwApba6fV68ctf/hI///nPWTFfjoxHd3c3vvvd7+KZZ56Bz+dj4zyRSOC9997D1atX4ff7YbVa2UmFDocDRqMRc3Nz7BRKu93O5i+d5lRfX888+eFwGFarFc8//zzq6+tx/vx5jI+Ps5p9dNIJRUEFAgHmVDSZTNBqtSxCJ5vNsn7dv38/nn/+edTV1WF0dBRKpRJtbW14/fXX8fOf/xyBQKDEMLRarTh+/Di++93vYv/+/SxFlQjXcun5tF4tG0jrwjisZPTzeg4/fnO5HILBIObm5tDb24u3336befy/+c1v4qWXXoLNZkM6ncbs7CwjvijSPhwOI5/Ps5NjqL4hX3yd9F86+ZJeZrMZXq8X586dw8mTJzE6Ogq73Y6XX34Zx48fx65du2AwGBhZSgQ4PWc5skYsKv8AqCpRQ20ppy+TnHhdGbibhCoX5UwOZv4efKQcHTzj9/sRCAQwNzfHanbV19ejrq4ObW1t8Hq9SCQSMBgM0Ol0JaflEYlEpRLIvqUT92KxGHNM0l6n1WrR2NiIjo4ONDQ0wGg03hXNzevdIunC95cIkYyi7/LvLZMT62IuEhEyOTmJN954A6lUCocOHcL27duh0+kwOTmJmZmZkhqklHrIjxdeV4/H45iamsLU1BQrq8BHQFIUKEV9E8SUTrofRf+QjkJjzel0YteuXaivr2ckuNPpRFNTEwBgdHQUfX19mJmZwb59+/Diiy+ipqaGXbtKKHsh5YkTJ1b60Yof8h3BN1RkgvmBSqAw34sXL2Jubg6zs7NQKBR4+eWXkclkMDo6CrVaDafTicbGRtTV1ZVMcLo/gdrBG9mit5wmBp1UxKf/UAi3JEnYvXs3zGYzq59RKBQQCARgMBhKDIx7GUzixAKw2mgaAPg/V/OlVeIE/Ydnc8UTO8RnEj/jT3PSarXM47O4uIh8Ps/Cx8ijQOTN4uIiJiYmMD4+zgo80YLLh7DxxYmJcNHr9cjlcnC5XHjxxReZ8kUKD5+mRKkdPp8PkUiEEXB8uD4/AWkRMJlMMJvNmJ+fh9vtZvV1RKNfjNIQNyORmFz+XbXkeAIoZdv5e5WLnuH/LSdP8fv8XCGm+8KFC3jvvffQ2tqKl156CV1dXdBqtYjFYpiammJkKr+B0eZGBBoZDIlEAk6nEx6PB7Ozs6wdVECMvFW02JOSQjKn9JzBwUFMTU2xQuA8RJmUm6PlxjnJT0xxrLIMAWFNpWct18ZKqPQZzR2eJEsmkzh9+jTOnj2LI0eOsDlEGybVRRgdHcXg4CD0ej0z0qjvc7kcPv/8c2zfvh19fX0stNtsNrN84r6+PhQKBbhcLnbsOh0rbDab2Ylrly9fxtjYGFwuFwwGw10h6Q8C8bcreIWrOhcfBnzEDFBq2BcKBdy+fRu//vWvcePGDWzduhVdXV3sFJhIJIJsNova2lo0NTWhUChgenqakeQmk4mdbpBKpdjr1q1baGpqQl1dHVMwa2trUV9fz1IVc7kcAoEAK8Ln9XpLPIVkrPh8Ply7dg0+n49FIlL7eeW7Ct5eEVWbi+l0+kS1rgXcvScAYCTlyZMn8eGHH8LhcODHP/4xnnrqKYTDYRZxFggEEI1G2dyiaFKVSsWiQ+nvRCKB9vZ2zMzMlNRqo9B8g8HA5j/JzmazYWZmBkNDQ7BarWhqakI8HsfWrVuxe/du3L59G729vaygMREC4loqyvJ+ZEvfXa4X+MjnIs0lpVKJUCiEX/3qV/jJT36C6elpNp7JAP/yl7+Mv/3bv8XWrVsxMjICh8OB2tpa3LhxAydPnsSNGzeY3singJpMJqRSKUZ+SZIEq9XK0mqUSiXzwJP+odVqmW5CRYgVCgUrus8TzXT6Hj1LOp1mOjARQ2TQ+3w+TExMwGKxoKurC6FQCPF4HIcPH4bVakV/fz+rBUHjaWxsDENDQzAYDNiyZQsL368UOU7/f9T74mpB+7Zo6ItRfaRvZrNZ+P1+DAwM4OzZs3jrrbcwNDSEL37xi/j7v/977Nu3Dz6fj+mC6XS6hAyxWCxobW1FR0cH6uvr2emHFouFFY3u6OiAy+WC2WyGSqViDkqPx4NQKASr1YoDBw7ghRdeAAA294aHhxGNRpmOSvNeJIDLzcNK83OVqIocM5nMCeBuvbTcHiCSaPwz0m9Ep6SoowJ3SLfBwUEMDAywOpmFQgHNzc3Ytm0b9u3bh56eHnR0dLCizjt27EBPTw+2bt0Kk8nEUrwBoK2tDfX19aivr2enCjc1NbEsAfqbr8OZTqexsLDACtOaTKYS24SbM6wPeEKCnlEEH3XDf847QpbXlTWfi+QUpKLBCoUCR44cQU9PD7RaLSYmJjAzM1MiZ55kpfdEvT0YDLI6XOQUBu6sUURY09pK0Wz82kDBGtTOcvWGKFKntraWpcIlk0kkEgl2UjHpxlSgnwoi3++8q2SboMJcfCiihr9pOeORQAOUXzQDgUAJSdPW1oZnn30W4+PjcLvdaGxsRGNjIywWCzQaTYlCIirhfFoOH/rNG9q8kMgIpMlOvyfCJhwOo7a2Ft3d3RgbG8P8/Dz0ej08Hg+rxi+2oZJBWC76YpVCrdrEKxQKJyRJYqF7PKvLt5P+psnDT4REIsEWIrvdDpfLBZ/Ph+npaRgMBnR2drKIIyJdKO93cHCQFZXNZDIsdYlfbDOZDCtaSp4cCi+NxWLYv38/6urqmAGYSqUQj8fZRAqFQiznkTxSVGBqcnISZrOZeZnp2nq9HhaLhZ0iVVNTw4geKsbIK2XUVzxRyG82/N/cfKiaQsovauLYEu8votwGz6db0IZA8yWfz+PChQt45513sG3bNvzRH/0RC5FOJpMYHx9HIBAAABZmT9XZaRPhFSVSMm02G6LRKMLhMFs8KYKF2kCGPhWD5r17Op0OjY2NuHHjBiYmJtDR0cFCV0UCVFRweOWT5iFfOI4/xYbvx8dB1JQz8iqh0uf8GKV+/Pjjj3H58mUcP34ce/fuxcjICEZGRhghRkqhxWKBJEkwGo0srYb6j0I8yQikYuCU7uTxeKBUKtHd3c2KsZEsqMDm9PQ0q1czPT2NoaEhtLa2Mi/Ww2AjETW8B5A8sXwNtFAohPfffx+ffPIJWlpasH//flYfgeq0UYh2R0cHnE4nUqkURkdHodPpmIGgVqvZqTOhUAiXLl3CwMAAnE4n2tvboVAs1U0hwk2v17M2RKNRzM/Ps7B/u90Oi8WCmpoaOJ1OpNNpBAIB1NbWwuPxoK+vD4lEAg6Hg5FvPFnzsPIVsG6JGgKtK5FIBMPDw3jttdfw4Ycfoq6uDidOnMDRo0dZvZrJyUn2far7xJ8KQ9G35FjSaDTscIXZ2dmSOjJUu4T0HX79o1OmqEZVNptl9fcaGxtx9OhRTExM4OLFi6zeEEVn8c91P31Q6b1HSdSIjgdJkhCNRvHBBx/gpz/9KTvViSLvtm3bhr/+67/GX/zFXyCVSmF2dhYtLS0IBoP44IMPcOXKFdbPdBQzzR2tVsuisMkALxbvFLoE7pxQwusMfJi8RqPB6OgoW2dpHwXu6GHF4lIRf6VSyQpNE4FAqagUKU5OsebmZvT09LBaVgcOHEBzczP6+/tZyg7tE/Pz8xgYGIBGo0FraysbQ7y+Ier3y3+vC+OQ+khwrpQYf/l8no39vr4+nDlzBm+//TYikQi+//3v46/+6q/gcDjgdrtLygGQI4pSaBYXF5njUKvVYmZmBpOTk/D7/bh06RKbz8PDwxgZGcHc3ByamprQ3NwMp9PJTpTy+XyQJAnHjh3Dnj17MDQ0hIGBAVy/fh0ej4fpnxRNx48fXv8TXw9I1lSNqOF1GNIlK+k3oqHOp/nwn9H84dP2FYqlkycnJydx69YtTExMIBqNshNdu7u7sX37duZMosg3cvp5PB5MT08jk8nAZrOxKByKZHrjjTfwxhtvYH5+ntkztKdGo1Hk83mWbdHY2MjWGZqDdCocRXjQmizq8Hxf0L9iHwJ3Iox48H+vNVFD/TozM4P3338fBoMBBw8exJYtW6BUKjE1NQW3283kyMuYd+jwwRUA2HzxeDyIRCIscp90DD6qkE83pH6k/ZNIHJ4rIJ2fvkMlG8xmMyNkgKX9isaJzWaD0+mE0Whktazo5L/7mXsrfO/RETViSgHfGNFjSiRNb28vpqen4fF40NPTg+7ubty+fRuFQgGdnZ2stogY5sRvwPxAps/4DYYna8h4pPQQkc2kAaJWq2E0GhGPx5HJZLB//37E43HcunWLnURDhs1qctJEFnQtiJpcLneCP9mnUvv49+jZyCtPC1lNTQ3sdju8Xi9isRg6OjrQ1tbGTkoiBXVxcRFjY2OYnZ1lCx2dFMOHx9O9KLQwk8kweVMdGjq9i7yJdDQihR9SbY1IJMKK79lsNpY3ykfw8Ew3Re3YbDZks1lWk4FSscoRcpUgLrCcwf9IiBq+XeJc4A32e7UTwF3XAoDBwUG8/vrraGpqwte//nU0NjYyOXm9XlZHQalUsiNEeZKDjAbafGke22w2SNJSShUZiuT9JUKWPMEU0UHFSinsmNLabt68SWH0JcoMb6jzG0GlDVJcp/j3lg3Nx0LUrBaVvktkCrA0d3p7e3HlyhUcO3YM27dvx8jICCYnJ1m1fUoHJXnRqXe8QUL9QV4Mipzg5a5UKuF0OlnKIRFewB3FkuSq1+uxbds2TE1NYXp6mhUvXsHDcN/9sZ6JGnpOcb5QxMvJkyfhdrvR09PDjvElZSIej8Pv9yMcDqOtrQ3btm2DwWBAKBTCjRs3UFNTg/b2dhbp6PP5MDo6ivfffx81NTXYuXMnxsfHWRoGrRnkzae2pVIpzM3Nob29Hbt27UJbWxu2bNmChoYGmM1mBAIB2O12PPXUU9i2bRvsdjuCwSA7HrympoYZozzpXyWsa6JGqVQil8shHo/jxo0b+OCDD/D222+jo6MDP/7xj7F//36kUin09/djenr6Li8fkdekRJLOQvW6ALBUUkqTonHEkzN0XSLPk8kka59KpWKpPVarFWq1Gg0NDTh8+DCi0ShOnTqFaDSK+vp61NTUlKQRPowi+jiIGh6kd1y9ehW/+MUvcPHiRUaO1tTU4JlnnsGPfvQjHDx4EL29vZAkCU1NTbh16xbefvttTE9PM+887UfkieX3OIqOIdIbWErvot8WCgUWcUqHI2QyGRgMBthsNng8Hka40X5A0aWU+huJRKDRaNDQ0MDWD9JRRFKOimDa7Xbs2LEDkUgEAwMDEuMs6gAAIABJREFU+MIXvoA9e/bg9u3biEQiAO7sj36/H4ODgzCZTOjs7GT7guiMEmS75kSNGIXBG2qEZDKJ+fl5jI6Oore3F2+99RY+/vhj2O12/M3f/A2++c1vMt0/Go3i9u3b7HAEh8MBvV7PXl6vF59++im2bdsGrVaLTz/9lMmIPOsWiwWBQACTk5OYm5uDXq/H8PAwPv/8c3R2dqKtrQ1Go5Glsz3xxBM4ePAgPB4PhoeHMT4+jvn5eWQyGRZVThHqYpFeHqt19JRBVeSYy+VOiMQDtYv/V3QU0/fJPqH/8ym1fIRUJpNBIBDAtWvXMDQ0xNJXDAYDurq60N7ezpy6s7OzcDqdCIfD+OSTT/Db3/4Wr7/+OhsDFy9ehNVqxb59+9De3g6lUon33nsPv/nNb+B2u3Ht2jWcO3cO586dw+zsLLMtDAYD0uk0O+2SnBjUvmAwyOql8hEX5Zyr4vzlP+dPtuLHNa/rLxMRazYXs9ksUqkU/H4/3nnnHQDAoUOH0NLSAqVSibm5OUxPTzOnKG8fiWNVJNpzuRxmZmbgdrsZIcPbsbQvGgwGRujRPsr3L58SRd8hPZG+SwQ51dQkwppIODrJ1mAwsLTUsbExjI2Nobm5mTkpH1LXebQRNYRyjDZP0kQiEXz22WeYmZnBwsICOyLr1q1bMBgM6OjoYB1H1+bJGLqeyMbSd/hCuXxOJ8/I8tfkr80z08TORaNRtLe3Q6fT4erVq9Dr9YjH47BarSUs2moXyQq1E8qhahMvm82eoDBcsX7JSgoVkTS0odTW1gIAxsfHoVQq0draCrvdXhJWtrCwwLwJ1PeUJ07yoAlBxgoRZ6TgUgiiRqMpWeSoHg4pnQrFUg5/OBxmuYv0G6pjQwurSqXCwsICy2OkSU0KMpEAlJ5D/SAWoyrXT/zfIvFXbaJGZNv5RY9f+PjoM5F8EJUbAu+BPHnyJHK5HCuqmM/nEQwGMTQ0hEQiwYpAA2AV8akPqNAaXTudTrOceqvVyoq7keJBSgiNBWo7eTBI4QaWvMbpdJpV3L98+TKUSiWam5tL0tPomel5xVxVXmY8iy/OieU2PpbUp4dl42mNI5Lm9OnTOHr0KPbs2YOJiQkWGUjjgIgWXkEipYGiZYjYppPXNBoNM0ZpQySChn5HcuBzyokIIKOmu7sbt27dgtfrRVtb20Md67yRiBqat7TuUF9+9NFHOH36NCKRCEuvpRQnGtNk7EmSBJfLhfb2dhY1Mzw8jF27dqGlpQV6vZ5F3wwNDcFms+HIkSOor69nBAIpGjTuKTogn88zo41OtiAjhea41+tFR0cHI2RMJhNqa2uh0+nQ398Pn8+Hjo6OEqPiAQ2IclhToqbSOsF/Hg6HMTQ0hI8++ghnz56FyWTCiRMnsGfPHsRiMfT19WF+fv6utYcMf9pvMplMyV5Nc4RkFQqFWDQjRRUSqUAED40vXik1GAwsZYBqA6rVajgcDjz55JOIxWI4ffo08vk8GhsbS06kWS3WiqghuZCTZ3FxEb/85S/x9ttvs7oyHR0d+N73voc///M/h0ajwdWrV6HT6dDW1oabN2+yAphEiPDkM3ln6WRR6mc+jJ76nV8HRb2vWCzC6XTCbrdjYmKCvWcymdg+nEwmWfSxQqFgKY1U94QMA57oTyaT7JCF2dlZAEu1a3K5HKanp9HV1YWnn34aMzMz8Hg8JeMrHA5jenoaW7ZsgcvlYobKCvvjmnvxeduD3/sBMJ1lcnISH330Ec6cOYN33nkHw8PDOHLkCE6cOIGnn34aWq0W6XQaIyMjePPNNxEKheB0OpFMJpFKpZj+Q+Onq6sLXq8XwWAQ8Xgcer2eOfxonZSkpePdTSYTHA4HCoWluieLi4s4deoU1Go1XC4Xi/Soqalhh2D09/djeHgYQ0NDAJb0Xko5rWTUrzTfVoGqpj7x4GXEO9CpfWKkFu/M5l+kI+VyOczOzmJwcBAejwcA2CEGu3btYjXxJEnC1NQUmpqaMDQ0hH/6p3/ChQsXEAwGIUkSI1TppEM6+GRmZgYffPABwuEwzGYznE4nc0JTwfdUKgWr1QqLxYL6+npYLBYkEgnU1taira0NSqWS1fIcGRlhB2KU03FoveBrIoq2L29T0Hf4FP1lkvGxzkW+jXRQy4ULFzA7O4tDhw7B5XJBrVZjYWEB09PTzNEn6t5i5But3fRZKpXC9PQ0/H4/00/JsUvXJL2UX28VCgXS6TQj1sm2AMC+y9+fxibZog6HgznqSUcnsobsS7vdDoPBgIGBAfj9frS2tpas+w+IR0PU8AOrnGceuDPg4vE4Ll++jPHxcVYJuqurC8PDwzCbzczYot+L0TI8+FAwfgDTwBVJHN4Q5Nk6PtSON2glSWKnTAWDQbhcLnR0dGBwcJBtonRcqcgMVvr/fQqwqqlP5DHgByO/EPILP0+ohEIhFpEyNzeH+fl5mM1mtLe3s+gIyqOemprCZ599hqGhIXaSEykaVJ2bigZTZAzl5PLhppSrDYAt0E6nk6VNUfoaRcH4fD4kEglotVqWF0x54xRlQPcPhULwer3MA0JKLEXiKBQKLCwswGg0wuPxMBKCX1D4fGjRy8T/u/yqmkLK35P3xosEjvgS207t48c+KYP5fB6ffvopLl68iG9/+9tob29n3teJiQlW/Z6MA37ckJHOn/5CY4MMCqvVyogbah9Fb5D3l9+wNRoNM1iy2SxTVJVKJau1cenSpZLxSM8oKpc84VZp7eDfpz55HKlPIsQ1hH+VA78GT0xM4M0338TevXuxd+9e3Lp1C9evX0exWGSppDQPSV60idGmRrUSqE8ooo33PBAhx9+bVySoT3kPhyRJiEQisFgsaGlpweXLl6HRaNjaz6/DD9of65moAe6c5EDKFh25/fTTT6OlpQVerxfRaJStVcFgkJ0MRIZBfX09I5RJEaIjtClNKp1OQ6/XY+vWrcyoLBaLuHTpEubm5ljKGpFyRqMRCsVS6u+tW7egUqnQ2trKjmvP55eOmvV4POjq6mLfB8Cuo9VqMT4+zsgbcX+pAlnz2IkacSwCpQQvPw7dbjf6+/vxySef4PXXX4dOp8M//uM/YufOnYjFYhgfH8fU1BRb5yjCg+YOyYInl3kilfZmo9HIFEcaR9x6BeCO55qiM2jMUUoptSEYDGJsbAypVAptbW04fPgwBgYG8PHHH7Oi0xSltVr5rRVRA9yRVzKZxKlTp/DLX/6S1UZQKpV4/vnn8b3vfQ8qlQoDAwOw2Wz4whe+gEgkgnfffZfJh/Yd3nlAJ/yQ0cwTqbxBSs4p4I5ezK+blFpmMBgwOjoKk8nEyJ5EIsH0JzJcALD+VyqVLI2DrzkDgKUeUqrx4OAg4vE4jhw5gkwmg1u3bmHr1q04fvw40+moBg6wdJqnQqHAk08+yYr1C3vhmhI1/P7AO15545X6LBqNYnh4GB9//DHeffddfPLJJ0in0/jOd76DH/zgB3C5XIhGoxgcHMRnn33GdFuj0Qi73c5IcCLqCoUC89xnMhlMTEywlH4AbD0GwMgfqnlhs9mg1WqxsLDAjg2mmh1kZ0iShIMHD6K5uRlutxtTU1OYmJhAOBxmui3twyQPgrgX8mvWKuZs1YgaXoci+YjRynyElhgFJepuPBKJBKupFA6HWV9s2bIFHR0dLBq/WCziZz/7GU6ePInFxUVMT0+z6F2LxYK6urqSGiWLi4tQKpXo7OzE1NQUzp8/z/RFimKLxWIoFpdSECcmJqDX6zE1NYVEIgGXy4VIJIKJiQkYDAa0t7dDr9ezFP5oNIp0Os3mOL/e88QUj3Ikluho5K+zVhE1pA+Ojo7i1KlT6O7uRkdHBxujU1NT7BQ7cUyW0wnoPVrrwuEwxsfHEY/H2X6byWSQTCZLCHKyCfj9T6PRwOFwsPRdcioSkUp2KUXEUSSjUqmEwWBATU0N+x6t3fF4HLFYrOS49mKxiBs3brCyDDzB/QD6Tlk5rjq8YyXwSgv/N9/IbDaLgYEBjI6Owuv1oqurCz09Pbh+/TrLrQdKGVhesLx3nDYwPq2BPBt0b37C88wkGYTUTlJg6O9yk8ZqtSKRSKC+vh4vvfQSfve730GSJAwMDODAgQNMWJWEQsKuYvj3fYG8QNRnvPIs1q2h9tJGTxtVIBBg4bOdnZ0lR1xTOtvt27dx8+ZN1NXVQafTsSgIACyCgk5ZopxqUoaIeKEj83jDIBaLsSLRdBIUhXMnEgk2Hih/NJ1Os6JSlNNIR37z6VnxeJwZIjSBTSYTQqEQAoEAHA4H/H4/S+2gglWESguNSNpUC+WUJrqfGJ5M4CNJ+L/FeUJKTjAYxPnz57F79250dnayvFIaD+St5U8uKxQKLP+Tj9CgiCxSZGhzJOOEFlZaHKl2FDHmVGeFjvGj31K0Tjqdxv79++HxeHDu3Dl8/etfh8FgKFGseTmIHgqxb+k74sa4niHKPxqN4uzZs+jq6sKRI0cwNjaGXC6H1tZWRKNRRCIRRnCVI1V4rwWNL1L8xXxgMmKIyOSPRScZiJ5kkv3Q0BB27dqFo0ePspBwKhi/mUHjjwzvcDiMixcv4oknnoDJZGJHodNpI8FgkK1VqVQKCoWChc7Tvme1WvGFL3wBjY2NLIpJoVg66t7pdKJQKDCSx+12w2QyweVywe/348qVK4wsp7DempoadHV1sbWCilqm0+mSaEej0YhMJlNyjDPV9XrvvfdQU1ODhoaGVUVxbjTQOqtSqZhRPjExgZGREdy8eRNvvfUWAOAHP/gBdu/eDb/fj7m5OUSjUWbo03pKc4QMfN7zys9v0nFo7aNoRJ1Ox9rAk5T0L5HqABjRQLXZqMZJNpvFyMgIFIqlApDf//73MTAwgHfffRcOhwMKhQJdXV3rbl0U9zu+vktfXx87WQdYUtTb2trwrW99CwBw9epVtLe3w+FwYHh4GLdv3y6J1uX1SUrdJL2HIp/4VHpqDz/H+VRUipQjvYb+pboaVNCU1mAi7aiGHx+hRvclZwbpwjQeKE0rnU7j/Pnz2LVrF1pbW5FMJnHlyhX09PTg7/7u77Bnzx78y7/8C+ujdDqNTz/9FJ988glcLlfJtdfLvKW9RSRJ+T5JJpMYHh7G+++/jzfeeIPV7fnhD3+I48ePQ6fTYWJiAr29vZifn2cnMvFEVzabZRETkiQxgob2xFgshsnJSUbEUE2SXC4Hj8eDWCyGQCCA5uZmmEwm5mhsbm6Gw+FAPB5HOBzGO++8gx07dmD37t0o/P/UfVlwm+d19gMSBEFiIQGCBHeCFEUtlCVLlrXYsWU7i+0kjpO0jdMkddqmM5lJO+30stPLNledaacXnem00yR/PJk4TZNppNZu4khxrViLJYuUaO77BhA7iB0kCPwX7HN48JlK7ESW5HdGQwoE8H3fu5zlOc85p1TCc889hwceeADf+c53cP78eclAiMfj+NjHPobu7m6pnWgM3nF8UDborxoaLDOCZ/o9uq6WLk+h36+DocC2/zAyMoJAICCshvr6erS2tsLlcqFYLCKdTkvTkpGREWHTnDp1CibTdlcf7p3W1lbRreVyWVLxo9EoAFT4CW63G263W1LwzWYzbt68ienpaVitVpw5cwZvv/22dNj7kz/5Exw4cAADAwPyHSsrK8jlcjhy5AhsNhuAnTblRkBLBwD4utEXvsPBj99o8LrZbBaXL1+Gw+GAx+ORuoPJZBKpVKrirPL+9XcYs1r062TshkIhmSuWUaDuMnZ6AiCsR4Lp2p4FdmrVAjuZHbxPkghYN5U6hb5XMpnEwsICent7Ybfb0dLSgq6uLly9ehU9PT1SzP9O6sk7CtQYB1/b3NzE0tISxsfHEYlE0NPTg+PHj2NkZAR1dXXo6emp2Gy7pSNpYEb/zuvz8OtiQfwurUiBncWkg0IHg9/HCDANp3K5jLq6OgSDQfT29qK3t1co5d3d3VJMyrgZjZvwXho4uuUwsFM13BgNArbnjHVpaKTk83k89dRTQu/Ua5vJZLCysoJYLAafz4f6+noxHNmqjhEqpsxokI0ggG45yUPU2NiIra0tNDU1SbE3PgsBA3bIKJVKUkOIEQjWatDdEhjtmJiYQCwWw+HDhyvYIf39/ZiZmZFCVplMpiK9R6fm7TbeZzTjPQ19Dna7vjFSoVkOxj2oUx54j9yr4+PjSCQSeP755+XeGxsbEQ6HKxgpBNKMZ1N3q2DbZ4IsTLthFJj7Tzs9vFc+Cx0XUh0TiQRKpRK8Xq+ANidPnsRLL72E+fl5DA4OVjyfNgJuB6byPdqAuBeGzm8y9Hne2trCtWvXkEql8PTTT2N5eRmRSESYDvX19VhfX8fm5ua7Oi4xja1UKslZ2NjYEIOFbZ55/o2KT4PVGuDRjoqOoFVXV2N0dBRHjx7FwsIC3nzzTTz33HOSBnc7nfJhHtqRKJfL0pFrenoa/f39KJe3mUw+nw9dXV1Ip9MC1iwtLWF+fl4cjtraWjlDjGDZ7fYKpgZBFLYVZQH/rq4uNDU1VRglpPlTbp48eRKpVErWO5VKIZfLYXl5WWobWa1WAZHq6uoQi8UwNTUFv9+PUqmEH/zgB3j++eexZ8+edzFEPqxDnzc+U01NjRTOv3btGn7yk58gm83ir/7qr6Rw8Pz8PBYXF0Vfce1Mpu3OQWQcG0FmfV3KRwI1NFKpl6g3ecbIVKRTSQMyl8uJQ8+/MzVjaWkJe/bswcDAAL785S/jH/7hH/Czn/1MGFl79uy563P+Xoc2wsPhMM6dO4erV69WgFN/+Zd/iYMHD+LNN9+Ey+VCXV0dLl26hFu3bklarw4w6aCDBi0ZSKAjQOeB80kgG4A4d2zbnc/npQECGzBQbrIWW7m8w4BmHRxdu4YBLg3MWiwWqZlBXVwubzMaL1y4gL179+Lxxx+H2WzG8PAwNjY28LnPfQ6bm5v4m7/5GwGJAoEAfvzjH+PYsWN44IEHAOzI93s9qOd0gEmDalyPaDSKkZERnD17FtPT09i7dy/++q//Gk888QQA4J133sHly5eF8aLti0KhALfbjUQiIelLNptNUoc5z6OjowJsM3DIdSdQTtvZZNqu/eVyuaQoKe3dmpoa3Lp1C/l8Hg899JCklv7Zn/0ZOjs78a1vfQtDQ0MoFApwuVziHOoivUagBrj7tosOomu5otkyxmD4r3Lg+ZlEIiFdndxut+hIj8cjbP9oNIq33noLmUwGiURCCrMzXc3tdgPY7tpF/UVGv8PhQFNTk9iTtbW1UsOJ90U/JJFIwGazIZ/PS0ro0NAQMpkMGhsbsbKygp///Ofo7OzExsYG9u/fj7GxMcTjcYRCIdy8eROHDx+WOoH6Gkb7VIOQt7Nff0PWxm896BdubGxgamoKMzMzOHLkiDQeYPMZo1+un1PbnzrrhXqxqqpKCvsSWOGe4Jkym80StCLbnoA1WXHs5kSbtVAoCItGnxveA2Utg4q8J753c3MTsVgMVVVV8Pl8EpAKhUI4f/48PB4PGhoafm3txfcDtN0xoGa313gj2WwWo6OjiMfjsFqtOHHiBGZmZmAymdDd3b0rSGMEY3aLhtPw14CIppQDqFgQDio2I21V04z5dwpDGj5ra2t44oknsLi4iNnZWbS1tUkeo3HsBtzci6GdeG7u290bhW02m5XWrOFwWIpGcm7Zko7t2ILBoLAZMpmMOBI0LLQjSKVIMEanWjEKROMf2F6v2dlZAQcymYxU8abTWSptF+zT6TEEBUiV0+lNDocDAIQJdPDgQaEz19XVobe3F+Pj43A6nWJgkb7IeeT4dQfxTq4jUBnt4/V3i6gY71GnFRnvnU7blStX0N3djdbWVuTzebS0tAjdj0KXgpJzqbsl8Syyjk2pVJJ1ZEFUKkF2pwEgjoJm5NAg4zUBCB2ZRdrC4TBaW1vR2dmJa9euob+/X4yl3dbFSLE1Rp+N83k3jdPfRD7wHhkNunXrFh577DGMjY3hrbfeQl9fH9rb2+X52EEIqCzqR0CFsoKggla05XJZKPfG6BkdAh0Jq6qqqgAVNYhAJoLf78exY8fwi1/8AjMzMxgcHKx4769jYNwPzsN7GVp/cf5u3ryJW7duobm5GZcuXUJzczN6e3vR1tYmLZS9Xi+y2Sx6enrg8/mkBgVrAlVVVcHj8VQYMrlcTijXpF+Hw2EMDQ1hfn4e+/btQ0NDAxobG8X5ZKcvGjk+n0+MKxo6pVIJk5OTOH78uJxpRjHJqozH42hubsbg4CAKhQKuXLkCp9MpaVC7GaT389gNEOfQbItQKIRAIICLFy8iEongD/7gD/DZz34WhUIBKysrmJubEwq1ZuHoVCQ632RdAJXpS/qc8ayxyCyHpn5TdlJXkh3KyD8ju1xbGqZWqxXZbBaf/OQn8fOf/xyXLl2C1WpFc3OzdL2g8aqDALvN091aZ16nuroasVgMP/zhD/HDH/4QiURCdNSXv/xlnDlzBpcuXYLT6UR7ezsuX76M4eHhipavZB/pfarTy3TgqVgsirNos9kk3VDX9yI9P5fLiV1pMpkk5Yh2FPcHiwTH43Gxn2i/0PjXbB2yqwgGEJja3NwUhsfMzAxGRkaQTqfxxS9+ETU1NZicnES5XMbTTz+N8+fP4/z58/K5oaEhvP766/D5fJIGey/PrFEfc234T5+TXC6HUCgk8q63txd/+7d/i0cffRS5XA43btzA8PCwdLurqalBJpMRJrYOLjAqz257TqcTkUgE8Xhc5CRb3VPGWywWdHR0wGQySRehXC6H6upqAWp43jOZjLBtbt26hZqaGhw4cAB+vx+tra144YUX0NzcjL/7u7/D1NQUzp07h+bmZjgcDrFFjTIC2D3L4YMeRnaMXhfKCdoHDLby3vTnOLa2thCJRDAxMYFkMimdCQcGBuBwOBAKhdDU1ITx8XFcuHABxWIRra2tiEajUu+rpaUFs7Oz4uwTJC8UCshms9LNh6xQr9crtZ/IrltfX0draysaGxsRCoVgt9uxuLgIt9uNVCqFzc1Nqflmt9ul0YrL5cLm5iYOHTqE2dlZhEIhBINB3Lp1C4cOHZJUfa6hcS60/NF1qDh/XPN7McrlstgWU1NTOHDggKSV5fN5zM3NIRqNviuwYfx9Nz8FqGy/nsvlUCgU5MxoYFvXoCEgrjsHazYpgXDOmT4vGnegb8E10GCjvr9YLCbp4ezCeevWLYyMjODEiRO/KgX/fY87BtRop1FPfLFYxNzcnLTX+vjHP45YLIZsNovOzs53oWl0FHS0fjcBpIUBW3dzcahMCdroFB0OvUC6UBwVni7kR8eFNKhSqYRnnnkGP/rRjzA3N4f9+/dLPptWIL/Kcb6bQ29C/tT3qeeLTjgNy0Qigfb2drS1tQGoLB7FoonJZFKotjQ46IDn8/mKVBcCKuzaBOzkRdOQ4UGjseNyuWAymYShQQOVAjefz4sDwz1ARodmbvCAs/ZOQ0OD0JHj8Ti8Xq/cZ2NjI1pbWxEOh9HS0oJIJAKXy4VsNisgz90cRgHOuTSeCw59lvR67+Z4EC1mzvTzzz8v61dTU4NYLCYKieeA7UG57ro6vQYG+betrS2EQiFxDnT7ZwCydmRjsWiqkRHEzwQCAezZsweZTAabm5s4duwYvv/97yMWi6G1tbVCQGohq/c9/6bTErWMuJvR/9/0Orzvzc1NvPnmm9IVZH19HQcPHoTNZhMjhOCpBmX4HVpmETjVhgDZUHptOYxGM7+XNYuMaZ8EUaurqyXf9+jRoxgeHkZ3d3dFfZzb7dsP0+CcMnpaKpUQiUTw9ttv42Mf+5hEaMfHx3H27Fm0tLTg6NGjaGtrQ1dXFxoaGiSyW1tbi1QqJc49AJG5dLZjsRii0ShmZmbwy1/+EjMzM0IVr6+vlyLBdFTYxbCurk7YBBoMYJ2pyclJpNNpdHZ2ilMaiUQwOTkJl8uFlpYW7Nu3ryJCGYlEcO3aNTzzzDMV6cnA/c1We6/7jfowHo9jZGQEk5OTOHLkCP7wD/9QuoJMTk5W1J6h0U0AhbqJTFUC1gRa6IDz3PB88u/UmUxh0raNZg9TpxKQo8FvMpmE+cj2306nE7W1tfja176GkZERXL16FQcOHIDX65Xi/O9lju6Wo8gzlslkcP36dfzgBz/A8vKyyLaHH34YX/jCFzA1NYVsNouTJ0/irbfewmuvvSY1YZiSTZlDkIOBJaZRM6DDvdzc3CyOOlNBya6hHtWBIp4xAjNkJefzeWn9G4/HUVdXJ4xu6kOm/xoLTXMYC/uToZNMJpHL5fDaa6+hs7MTJ06ckG5QHR0d+MY3voGJiQlJLUkkErh8+TJOnz6NQ4cOAdhhoN+LsVvgRDuBwE6Hs9XVVdy4cQOvv/46LBYL/vzP/xynT59GOp2G3+/HpUuX0NLSAofDIeut9Q1tRqZD0X5taWkRO6WzsxNNTU1YWlqSVLhyeTuFhs5hVVWVMMrL5bKkO1ksFgEBWOS5sbER5XIZ586dQzKZxGOPPYZAIIDm5mY8//zzSKVS+OY3v4lLly7hyJEjGBwclLo2GtjV88Wfd2vNdCo7dbaWY3rovatT2SiPACCRSGBkZETqMLndbrS0tKCpqUm6ml29ehWvvvoq1tfX0dzcjHw+j0gkIuevtbUVi4uLqKqqkppLbM9NdjYZFg0NDbhw4YLIawYjuCfq6+tRW1srfgX1pclkgsfjQTweR01NDSKRCEZGRgAADz/8sHTHra2tRS6Xk0LlDz74YAV7Ttug2s7i2hr9USOYc7cGweXNzU2Mjo5ifn4efX19ohfY0p7yUNuFRrCDgzaiEbBju3MGf+mj6zozOnBlMpkEbGU6G9eFvoeuH6ZtEX2OeH6BHf+BZ4qyv1gsSidkdhljClR/f7+kot9uvJ91uyNAjfHiWkikUinMzs4iHA5LYTpGEXU6jqb8GtE3vSF3E0hUqlxkAGJg6oi/Fg7aieUU6DnbAAAgAElEQVQiaaNI07R1TRyzebt7UFdXF1wuFyKRCPx+v+Rx324+7nXak/HgE13kc1M4MHqQSCSE3t7d3V0RZacBPjo6KsaLRjw3NjaQTqcl0kcwRQNB7P4CoOJ3k8kkh44RDt57LpeTKD9/pyGl65yk0+kKwUowgXuHBivXdGNjA4FAQKLTNLBYx4H52uVyGe3t7RUpMndz3C7ycDuAUBs3u/1Np2EUCgVcvnwZTqcTe/bsEYG3srIi3SHoQBBQy2azovwIgG1t7RSDrqurk3NIhcjaDGxTypQogqB0WjjHPH8EfHj/2WxWOuQQnKmpqcHU1BRaWlpkXrjnd3P4NRUX2EHctTK5V2v9fgfX6cknnxTDD9hmMem0Px1x1zKBf9OgAkE5yglGtHTE3vh5vs610wqQ8oNGCec6EolgYGAAc3NzmJmZkVx9oyH+YR1ah/HMvf322/B4PAL60gAdHBzE8vIybty4Id2aOjs7YbFY4HA4sG/fPimcDuwYdfyZyWSwsLCA//qv/8LFixdRW1uLU6dO4fTp03C73Xj11VcxPT2Nffv2VQQnOMesNcXvpm6ls/iRj3wE3d3dqKurQyQSwdLSkrQKt1qtwswBtveE1WrF0NAQHn74YamNZARNP6zDaEyOjo6itrYWX//618XBe+edd4Rtoc+D8SflK50bsnm187O1tQWn0yl0/qqqKmGmEmShvtT7jfqOqU8MqBjtLLJV33rrLezfvx8+nw8nTpzAs88+i5dffhkXL17E4OAgOjo6hBVwr9fPaC8Gg0G88sor4iiVSiW0tbXhL/7iLwQY/uhHP4rV1VX85Cc/werqquxDnf6sGdI07GlPMI2CtQv4M5FIwOl0Atg589QddNLoqBcKBbGPGBCjw881i8ViUgCzqalJgB4NxOkgm94DpPgzUMaUjlQqhbNnz8Lj8aC/v1+Ydk8++SS+8IUv4B//8R8BbNtsV69exWuvvYaenh55/rsZvHg/g7Y95d8bb7wBv9+PJ598Es888wzy+Tzi8TjOnTsndozVahVWCoAKW0MHxWjTplKpigDP+Pg4rl69ir6+PvT390uR2mKxiNraWjidzgq7kzbR1taWpKOSOcN9s7q6iu9+97uora3Fww8/LDbRpz/9aVy+fBn//d//jTfeeAMnT56s6Px1vwyjTaFtp938Ny1DtB7K5XKYmZnB6uoqnE6n2DNer1fsyFdffRVnz54VJgtrXObzeeloWSqVpE4bA4x+vx/hcFhA6+bmZjz66KMolUq4cuUKNjc3pfAvnX4CdLW1tYjFYsKyZxFb1m7j8/zwhz+Uc/70008jEomgo6MDCwsLqK+vRzKZxOrqKvr7+0W/7qYP+ZomJhht+nsxGAwcGhqC1+uVWnmZTEaaj2jgRf/faIffDmQ0mbZZhzx39OXJAqZtbrfbZa0ZuKVdRX3K99L+oG7V90ffs729XfwJ7XPxvrTMJaOyvb0dtbW18Hg8GB0dxejoqGACd0Jm3jH0QBv/HKVSCcFgEOFwGMvLyzhx4gRCoRCsVivq6uokCm80GDjpGmnl3zQdia9pZE07p0zPoTOvUUh9XToMW1tbUrx0N/YNB9FEFugMh8MVTqR+//3gZPDeUqkU4vG4RHQDgYB08aHBXigUhPmQy+Xg8/nEcaPQZVoUW10DqHDUeViZ7wtAABpSc7VDzIOkuz0BqCjIlM1mK4xXHd0xUuF00ctyuSxOPZWe7lYTiUQQDocRi8UwPT2NZDIpz1tXV4f9+/cLK4F5ifdqaEPfuNe0UOR7tcAz/s7v4v8ZBXjooYfgdrulbgINWQocGvoAKujfxg5qnD92jOE6t7S0oLGxUYxeYNu4YuEuRg1Jz6cc4LV1JDMWi0mbWrPZDJ/Phxs3bkj0kUM7LcbzzzmhbKDi/bCABATAhoeHsX//fnEOyN5gJF3XxuDnjIws/tSOv1Y0GhQwzg/fZwSCuH4aUOf8agB9Y2MDR48exezsbEVtHKNS/7ANrY8IUEWjUVy6dElqaFksFqmD0NraiiNHjuBjH/sYenp6KtIeyuXtGjaMXBk7+dCwIU3/zJkz+OpXv4rPfOYz6Ovrg9PpxNGjRzE1NSV6IJvNIhQKYXl5GX6/X4B3rj2jnG63G48++ig+8pGPSH2bQqGAtrY2HD16FF6vF16vF01NTaivrxc9EgwGsba2htdee62iKN+9NDJ/26GNN+q7ubk5LCws4NixYzhx4oToi/X1dXHOtO3CPUFjT+ffU2eyaDqwXeuttrZWcu7tdjuqqqqk8P7Gxgay2aw4R9xrOgrIoIU29HUBft5DLBbDrVu3pGbOCy+8gMbGRszPzyMQCCCdTldEQ+/l0GzrYrGIyclJXLlyRZoHWCwWfOlLX8Lg4CDC4TAOHjyIjY0N/OhHP4Lf7xebQ9dW4/zQhqAeq6urk5b0TAdkYI+BqZaWFjlX6XRaUuJSqRSA7WLvS0tLWF9flzXMZDJiC+fzedTX10u3Et5XY2OjrLFmo5IdxcKdrNERi8UQiUQQjUYrGimYTCakUilcuHAB5XIZAwMDWFlZwejoKJ577jm0tLTI3giHw7hy5QpWV1cl2KJt9Xs5dmOBbmxsCMh95coVOBwOvPjii9JUYmhoCOPj4wKSkZWkmQraKeaZDYfDeP3113H27FlcvHgRY2NjmJycRDweF+b25OQkRkdHMTY2hpWVFWFa6PtkYIrfGQwG0dLSAo/HA4/Hg7q6Otjtdhw7dgzT09N48803Rfa7XC783u/9HlwuF8bHx3H58mWpAWLU4Ub/Q+v5D3Jov0zPK/eM9qk4J7oeCd+bz+exsLCAcDgsMouOts1mQ11dHVZXV3Hz5k0psk19RJkFQDqn1dfXSzDQ7/cLU6mpqQmFQgGrq6v4+7//e5w7dw52ux2FQkGKDLNINL+faTFMpyH7lzYqaxJRP7/55ptYXFxEa2srcrkc2tvbxf5dWVlBOBwWp1/PH/1Vzgmwsz+1/Xov9CjXaGxsjB38YLPZBDDh/Ot9qZlS+nv0vtR7QjNBObfadjSZttMKGxoaxG/I5XJIp9PCTiTzkAEQ2pU6jZX3Wyptl2hoa2tDX19fRfqrPmNAJROoVCoJkMTghdvtxs9+9jMEAgEB+Y3n7/2exzvGqNltEYrFIgKBgBQi7ejowMjIiBiav8pw01EDfQ29mTnROlKsCwNrYQHsgEmauaOLBGnqk94gRsFXV1eHTCaD/v5+TE1NIRgMIpvNyoYxzoO+9r0Y2mGi0Lbb7YIacx2o6IBtg6GpqUnq0nAeSPNm/iGpuxRkjFQQ7AAghZ14ePg6KWva8aPhoztikIJI5oQuqsg0LGBnP3Bd+VzZbBapVEoELKnMjGzk83k4HA6USiVEo1Gpp1BVVYXGxkYcOHAAU1NTSCaT8Hg88Pl8FY6MZhZ8kMPoWBtBG+2Ea6PDCGoZz0WpVMLw8DCqqqrw0EMPob6+HiaTSWr+6NZ3BMn4Op0EABXrYrzvYrGIrq4u1NTUSOqbdkS4hzSyzUEnlc/BHHyCfwRO9+/fj3//939HKBSqSKvk0GfTKAv0/uPgNe/3QbD1wIEDWFhYQKlUkq5nms5PUHU3oFvLYCNQw9c1q4lGi2ZIGFNbNEhBIwuAODKUB2azGWNjYzh16hQsFgvC4TC6u7vlO4wy+MM4OCfFYhHnz5+Xbi/c6zQMcrmcyDs6TZxnDTjq86Ajkg6HA319fejs7BTZSdCV6cZWqxWXL1/G4OAgNjc34ff7USgUcPjwYRw7dgxer7fCmWdtBbfbLWe8WCzC7XZL21mmg5hM2+3Xw+EwJicnZV/GYjFMTExIcVLjufuwDKMzRLB6cnISm5ub+OQnPymMFVLrU6mURG+pM7nunE++TnuGspVnRDMpzGazgNuJRKIiysrzSvYb9wvtI16L16XsYE0HyvhsNovl5WVpt9rZ2YmJiQmJlt7LNBg9yLplPYuLFy9ifn5e/n7w4EG88MIL8Pv9ALYBr/Pnz2N0dFSMdjJuAUjdCDpbLKLMovZms7liTcLhMABIqkwmk5E14Pnj2YhEItLmFwCi0WhFGpndbkcymRQnkdHiqqoqBINBZDIZCTJxbwAQR4R7kbU3qqq2ixonEgnpntLS0iIMgOnpaRw5cgSBQADj4+N48skn8dxzz+Hf/u3fxMmZnZ3F8vIyDh069K6Az90YlEH8nXqL60XnmClPBDiCwSA+9alP4aGHHkKhUEAgEMDKygoGBgYQi8XQ0NAgAI7u4MUzQxnHlMMHH3wQmUwGLpdL0rP7+/vh8XjQ2dkpe4Agpt1uf5du1TqstrYW7e3tko5qMpmQSCQkSFZVVYWVlRW88cYbeOKJJ1BfX4/jx4/js5/9LL773e/i4sWLeOCBB6TWmLZfNKCmX78ba6bZybwPI6C22/uo47e2trC2toZoNCrF0q1WK9LpNA4fPoz19XX8+Mc/xtTUFIrFIhoaGrC1tSX1YOLxuNjtPFf0A5gpUSwWEQ6HkclkpHZNMBjEhQsXcPjwYSwvL8s1x8bG0NPTI6UfKCPJFtfPtrGxgfb2dmxsbCCZTGJ9fR2JRALf//738bWvfU0ICm1tbZK1MD4+LqnHRkBG1/DRPm+ptFPmQ8/p3RgMJKTTaQwPD8Nut0v3XKfTiXA4/C6ghp/TdtxutrjRtwEgQHoulxP7k0wV+pmsYcP3k+Wi7SJ9Dcp8+g1kK/t8PrS0tEiWALAz3xyabcf1Z1FqnkOv1wu/34+rV6/C5XIJ0/G3GXcUqNETTWM+EolgdXUVDz/8sBwS0um5IDoHj5vQiAJrtG03ZgzfQxYNBbkejDySWsxr6p/6O/VicKOVSiXpMtTY2IhisYhQKIT19XU59Pp+77Zi221QkZNeCaBifnhvRPnJUti/f7+sBdHIZDKJ+fl56R4DQFKRqOh0m0IaEzqvkAaPMRdT510DO1RRi8Ui3RZooGiqL7C9fjSCNIDEe9f1cXioiabSqDWZTIK2sxNATU0NWltbsbKyAovFgmAwCI/HIwLkbhmsVGIEDna7ptFJ/lVpP/rz2WwWw8PD2LdvnwgbdpzRSDgZThsbG6ivrxdq58bGhiguHY3XEWez2Yy2tjZkMhlJnaIQ1Kwb/SzcC3p9NFOK5725uRnZbBa9vb3Y2trC2NiY5Icaz6DRcNEGjfHn7eb5fhurq6uSspfJZN4FdDEawfXRjpuRmszXKJd5XkkJZ7tFso80UMPvMM6ZjpgB72ZGMgqVyWTQ19eH8fFxtLW1VSjFD8M67Db0/iuXy/D7/VhdXcVHP/pRATkI1DBFRYNqAGTteJ7o6OtID9lm7LpAo7S6ulo61xUKBaytrQHYdkjn5uakKGZLS4sAelwrgnLUFbw+91Nra6uA7lwnOovRaBSxWAx9fX3o7e1FoVDA9evXsWfPHqEtG/ffh2loUIQdn7q7u/H4449jc3MTa2trmJqaqgga0KnU+fQa9NAMKbLiGFklMM71oEOuwRgygnlWeX50NyI9aE8RzN3a2pKOQcC2PUBG6aFDhzA2NoZoNCrfx3m4l4MAVj6fx8jICF599VUpVl9bW4svfvGLKJVK8Pv9OHLkCILBIP73f/9X0sf4j0AabRQ6idRrBMbW19fFFiTYTMYibQ6eedYGo2zb2toS22FtbQ3V1dXw+/0C0pB5ReYj10ynCVdXV0th2pqaGulCQxYbsJPCy/2mz6bFYkE0GpV0/QceeACHDx/GxYsXEQgE8MILL+CVV17B6uoqisUigsEgbt68iTNnzggDELh7tU+on7W+0DYObbtEIoGVlRWcPXsWt27dgtfrxYsvvijslcuXL8Nut6OxsRGZTAbj4+Po6elBuVxGS0uL1A7SoCmLPbvdbmFSADsFR3O5HDwejzjZ5XJZgDstQ3n2CcgwaL26uirpbHQ2W1tbAUBs8NnZWbhcLjzyyCNwOp34/Oc/j9dffx0jIyMYHR3F4cOHJRVvt7N4t9ZJXw/YYdhy/2lghvKKvgU/VywWsb6+jtnZWQnCNTY2IhqN4uDBg6itrcV3vvMd/PjHP5Y939raikAggOXlZXR0dKClpQVOp1PkGYOIPFt0nHl+AoGAsOirq6vxgx/8QHwV3u/c3BxsNpsU36cvQf9Dg0GxWExqdhLkXVhYwPe+9z187Wtfk7IMVqsVVqsV6+vrmJ6exsDAQEUgS/vA2qbajal0NwdttfHxcWSzWbE73W43yuWydAo22vJGe1rb3hq8Mwa+LRYLvF4v1tfXZR8x24P1oPjP6XRia2tLGinQRqUs12wfAtEmkwkNDQ3w+XwC7ukgt14DDt4j98HGxgZisZgE1vL5PPbu3Yvr169LHSKjf/N+z+QdrVGjD6TJtE2xjEaj2NjYQGtrq7Aa+ICa6UHDR0dw9Xfq79V1DihcdQ0G4N1tidm1gIZNJBIREGE3Z87ouOkDoa9vMm3n6mmQwBh14/feq2FMlTE+D+c0FArBbDZjcnISbrdb2g4WCgUpKnvhwgWk02l4vd4KNg6NT76fBcC41jRazGazFPBlOhKFEhk5pDdq1Jh1bAjwsZuTPkR0GnTBPeYKU5hSAXOvkPbMvcKcSFKYadx0dHRgbW0NhUIB6+vr8Hq9sv6cQz3utADdzdnVwlvvOb1vdYTICN4QLAkEAlhcXMQnPvEJiU4uLCxIRJiGK9POaDBqY1QDrnoN+Jmamhqsr6/DYrHIPTD9iQAqr7WbU88obyqVqih0ylaasVgMbrcbe/bswdDQEB577DEB93hvRhYN8O6CwVpxaEfqfhp67Tc3NzE+Po7BwUHEYjGpfL9bnRH+X59/ozzQFFM6ido4oSOjIw5aCe0G1FD+8B8BRxpDBGKDwSB6enowOjqKUCiErq6ue2KM3Mmh91K5XMbc3Bza2trQ0dEhkXw6ZXT49FnWZ4JrSkeMf9O6SzM3isUiMpkM1tbWpKD/yMgI9uzZg0ceeQQmk0lAdVLM2XJUByi4B2jEEEjSZ5X7hSkYLEJPGjGZCLOzsxgcHHxX9PDDNihTWN8sHA7jK1/5CjweDxKJBKanp1FdXS11PdgqnXJTR/l0rT7uBw46AsAOWA9s2zMEDwBUMIk1sEYHyGQySe0Snqmqqu0Cm0ynYpoFr8UOfRaLBU8++ST+8z//E5FIRBgM94NcZPAvk8ngypUrmJ+fl7176NAhPPXUUxgfHxdb5ty5c8Lwpp1BxgvXRNuArJ2mGUgMLuRyOTgcDjlHDDwwXTGRSMi5qK2tlboHtI1sNpsUpGWB0ubm5orCwSaTCW63W+rYrK2twWQyoaenR5xOgnS1tbVoaGgQBhDZwgBEb7JgcTqdxujoKI4cOYIHH3wQfX198Pv96O/vx7Fjx6QQcz6fx+TkJAKBQEWg726vsQ6capnB8xAKhTA7O4uhoSGkUin8/u//Po4dO4ZCoYA333wT6+vrEi3v7+9HMBjE5uYmVlZWKnSk1lM8Iwx6mM1mhEIhcfjpoDU2NoojWC6X4XA4RA8TqCEwUVdXB5vNho2NDTidzoqajg0NDVLUPZfLCeDg9/sRjUbR2tqK/v5+fOpTn8I//dM/4a233sLp06fh9XorAjO/jUP42wwGgSjfNPPDeC8a8AO25VcqlcLExESFrTEyMoIDBw7A7Xbj7NmzuHz5MqxWqwTFV1dX4XK5pHi+z+cTplSpVBLdQ/m3tbWF9vZ2JJNJ3LhxA7FYDFtbW/D5fHjmmWewsLAgtkkoFAIAKRTOdaHsJwucgDdrUPH5Cewkk0nMzMxgeHgYx48fR7lcRjwel3OdzWaxuLiIzs5Oqa1iBNc5dzqAc7ftIso2Fhr3eDwVAHcqlaoAvXYjKWh7fLe9aXzNarXKeWXNrkQiIQAgC3fX19eLbKM+45nmvuT7KfubmpokZZu2qNa/xvnVfrTWsVVVVUilUvB6vWhtbcX8/LwEo65du4aOjo4K/+M3GXeUUUPDjg+QSCSEwuZyuZDJZCqqoBs/b/ydioLOtKbuAjupFmRi0CChQOc/i8WC5uZm6YpgMm1X6g8EAhWHbbf70I4un9FsNgujoKGhQQQphfH95lgYnTMNRHEwrzkajSKRSOD06dPiHLMF9/T0NFZWVtDQ0FDBLuLgATTS0YAdhF07HQRCOGe8n9raWkGXNzc3hZ7NzjNUgKxbYzz4NGi5dyg4dWSJkUldMIrAz9raGpqamuBwOORAer1eRCIRNDQ0iAGka0MYh3F+f9ux23nRdWE0sMj36/OonSptZJfLZYyNjaG+vh5dXV0wmbbZNIwKaAPpdgZAdXW1pKppx57dL0ym7QgV2/g1NTUhGo2+ywgCdqL2mg2jI/V8j64JtbW1JRGUwcFBvPzyy1hbW0Nvb2+FY2QEW7kPjYAbHeHdWHn3eminzWQyyXltaGjA3NycyEmmpBFI1Y6hlr/6DGrnhCwOYGe/MKrPddFGxO1ARL0HeR8E2Kqrq+VMA5DcYp/Ph5GREXi93op0rV+n2O83uWsESBlxZSoSI62cD93tjntSR510qjBBnVJppwOezWaT1yhTWUS2paUFr7/+Ovbs2YPBwUFYLBZ4PB40NDTInmHdLg246xpkjJCyZgDvg+eERlNzczNOnTolOpJRLp/Ph/n5eRw8eBAAZN99WEe5vM0anp+fh8PhwLPPPotyeZs1lcvlBGThvCSTSWH8Aqiwa2hYahYFQQLONZ0XtnBOpVKw2Wwir3hOtezl/iPYoBmg2uHQBYkBCKsjHo9LbaOuri5pRb2bfrnTOu+9DOqmiYkJXLx4UebNYrHgK1/5ChKJBLLZLI4fP47r169jeHgYAODxeKSL2tbWFtLpNIAdajup9QRBNjc3JSXJarUilUohlUoJwMnoOcGwhoYGqR/EtaHxzg6VXq8XbW1tuHjxIhKJBOx2u6RdU79ns1lks1kkEgkkEgkBCsj24fmh3CaDNZVKIZvNCtOZqVrr6+vixHLOenp60NPTg6WlJSQSCXzyk5/EhQsXxOmamprC6Ogo+vr6Kpg+dxOo0yANr6uDa7FYDENDQ1hZWcHevXvxla98BXV1dZicnEQkEoHZbEY8Hpc5ttlsEvgJBoPSPII2AWtVspYNWb906KgjaYdQznm9XpGjdBBLpZ06FqwVRya3ybQd6GUaD/9GthYBWQafGhsb8cQTT+D//b//h6mpKSwuLuLIkSMip416Q/9fF1D9IIbW0bTHdwty72aPFQoFCQ4C20E8Mga7u7vx85//HP/8z/+Muro6qd/kdDqRTCaxsbEBu90Ol8slPp5uRJLL5UROMuC4vLyM9fV1Of/r6+vYt28fXnzxRfzLv/wLHA4HDh48iEgkgvr6egFdWF6BOpophhaLBa2treIfsCYVg8GFQgG/+MUvMDg4iLa2NklJZG2iUCiE2tpa1NfXV3TFNPrKRjuf17gbg37VzMwMkskkfD4fNjY20NTUhLq6OiwtLYm9rwPGxkDd7b6b51nLlaqqKvj+r07qq6++KrKLIJzNZsP6+rrsN72/AMh+oe4ksK5rBfGMGu0tnh+t4/RP3h8ACXS4XC4sLy9jY2MDPT09uHHjBk6fPi24x2+6Vh+YlbS1tSXFzUgdpENBhxjYEcDa+Nc/mZ9vdKT4eRqmGnDRaDajHVarVQQlv99msyEWiwGobCtsZO/oXEEeVgphtlAkMms0PG/nYNztYXTgtZFVLBaxtraGXC6Hubk57NmzBw0NDVKjxGQyYWFhAcFgEP39/ULl5TyRhs3v0s4hU2OMkT4eDuMBpoPBqFShUIDdbpfDpmuZ6Ag910Q7nozwMjLBe6LDoWnKNGotFgsSiQTm5ubQ29srxhaLCdLQSqfTYqQZQQw93x/E2C3qo/8GVBbG0++hsOD71tfXcfXqVezfv18ii6z5Q+dNC18jyKafk+uggRruMUaTdc0bnivuCzoU+m+aRUD2jU6RI2jT1NSEUqmEgYEBlEolTE9PC/Ckn99ovGgwViuR+zXSr5+DXWUY2eDrlKmcu2w2i3K5LI4zhzFvVitLGjkmk0kcSzoDAGRPEDDg5zWriq/xs9qR02w5ffZKpRK6u7sxOTmJWCwm1Fqj8t9t3AtH8dcNo94ym81YX1+Hx+MROUlDjsY/nQJSZllTghF1t9uNjo4OqWWQz+cxPz8Pp9OJgYEBYQnwH1NA29raxBBkVJKMGq3beJ3GxkbJyWdtMhpIrG9GmaxBPo/HA6/XW6EXqqu3a3gsLi5K147d5Ob9PoxsvEwmg+HhYZw8eRI+n0+cAMo02joOhwM2m00KxzJdlPuVcpFjY2NDghWUm4zE8zvr6uqQz+fFCGS0l9em/iVTlcCQtr0IBFGOApD7Yxv4rq4uuN1uHDlyBJOTk/LsGjQG7g1ruFzebpLwxhtvYGhoSOTDQw89hNOnT+PmzZtwu92oqanB0NAQ0um07D3aftST2Wy2wtB3Op2w2+2yt5lexP9T1xEw0WmLdMhpHwLbMo9pWeVyWcD1VCqF7u5udHR0CDOHKb5MiyGgSmCWep1MGrLL2bAhk8lIdJ+MRQJvmsm1traGmZkZPPzww/B4PPD7/Th27Bh8Ph9u3bqFYrGIlZUVTE9Pi6NP/fJBAzW7BXQ5d1pvM61zYmICJpMJv/M7v4P+/n74/X7cuHEDDocD0WgUoVAIDocDnZ2daG5uFhYTm0SYTNvM4XA4jEAggHg8DpfLBa/XK8xq3fmypqYGvb29UqyZc00Ah3ZMPB5HPB6H1WqVWkc1NTXyHXa7XYpUZzIZ+P1+kb9tbW2w2Wxy/62trTh06BAOHTokTKd8Pl9RV0PPm7b7qd8/qHXTtuVu4IK2XXRArFTaKa7c1tYmYKLJZMLRo0dRKBTwyiuvwGQyweVyie6pqtru0kpbgja9DkxTx/LcMs24tbUVbW1tyGazuHnzJk6dOgW3242FhQUp9N3d3Q2Px4PGxkY4HA4BZcrl7WbEKxsAACAASURBVJQ7nSpcLBblvLNeKdfZarUiGo0iEAjg5Zdfxh//8R9jz549uHHjhsyHxWLB4uKidB3SgTINfmk7np812lwf1NqycPaVK1fQ1tYm6+1yuSrAbt6z0c/Ur/Pe9eC+0HYe93J7eztOnDiBxcVF1NTUCPvM7Xbj2rVriMfjcj7dbrcw9xsaGoRNyRpBmjms7R76tNqe1femASAjKYCEArLrAMDlcmFmZga3bt1Ce3t7RYe59zs+UKAmk8kgnU7LDRppRbtF+zX91GgI6N95MI0OFSmfmkpOOhk/RwFP59343dyABHzsdntFJxlG0iiUGbnQkSY97idjVAtzziHrkbDVZ0dHhwghk8mElZUVZDIZ2O12Ybcw4sa101RHnRpDw4HdgbQhxAgQ/2knjgKOYACFJL+L0WM67hpQ00wAAEgmkyKoNZDBQpu8DzoaVVVV4vjQGaqpqYHL5cKtW7cElXW73Xd1bY3X0tE0nie9vkahAuykBFIIzs3NYWpqCp/5zGfEKSQYxnoKvBYBEhogVBz8R2Glo+z8PM8TACmgx+gU9w5QCczo88aIb11dXcW5J7WcgrlYLKK/vx9jY2N45JFH5N70ufxV0V+tJO43px+orFXEVBIWeuT55HnhPHFNtIFNQ4ffyf3AyANp2jSYgJ3CbnS+q6q2u85w7gnscl2pZLXS5tnX7wFQUU+qqakJHo8Hs7OzaGxsrEj/uJ9k6Xsd2rFvamrClStXpF0uZZousk69VF9fL92gSI/+n//5Hzz33HNwuVwiJ4vFIiYmJrC0tIS9e/fi0UcfRW9vr/y9q6sLTqcTn/3sZ4WZ2NHRUcGm4fldW1vD1atXsbW1hUceeURkPFMllpaWcPjwYUkjoNNLAJvpPmR+ZLNZrK+vS32PtbU1xONxtLe3v+tcfhiGNj6LxSIWFxcRCoXwR3/0RzCZTFIwFtjp3EFZylpbus4W9Y4OWJFpRtYhv0vLXwaw6ADyHFFXcU4ZXNCymvdEGUHdrHUj3xMMBhGJROB0OnHs2DFcu3ZN0n/uh3Xb2trC3NwcXn31VWEtuN1uvPjiiwKkHT58GPF4HB0dHWhubpaCvZyPYrGIdDot9U74L51OS408OmRkR1VXVyOZTEpU12w2SyDByDqMx+Oii2OxGMrl7W4yBDjJjlpbW5Mz6XK54HA45BoEgKqrt+ux0DkwmXYKRyeTSQQCAQEJqKd5zuLxuMwZdXQqlcLY2Bh8Ph/cbjempqZw+PBhnDx5EiMjIyiXtxkJ7B5FmfVBrz3Pw+2GBgMymQzm5+exuLgIl8uFM2fOIJlMSsoTm2IQfAwEAmhvb5fgLWvP0F5l6j67mhKkaWlpqbAzCKzPzs6ip6dHgtLZbBZVVdvpECsrK4hEIujs7JTvKhQKYgPpwNTKygqi0Siy2aywRgi+ptNpTExMwOfzwWaz4fHHH8f169cRi8XeZddwaAdegzcfFFDD62sbkz6U0Z/TvycSCayurkpBVsrFmpoatLW1YWpqCplMBl1dXSgWi4hEImhtba2wUwnQELjm/VBOl0olAU9sNhv27NkjrLLW1lb4fD5cuHAB77zzDg4ePIgjR46If6FZSHwWMuqpP8l6q66ullQc+hPaX1laWsLly5fx6U9/Gg6HQ2SM1WpFPp9HNBqVNu8E1akjjHaynucPcnCO8/k85ubmEAgEcOzYMUnfa2hoQDKZFPae0Re5HXDHwefQwVfjMJvNOHbsGA4dOiTpxARIH3vsMWED5nI5DAwMoLm5WWwapoITQKdvwrOo5Zlm0Ojzo8+W0d/i67lcTrq4BYNBWCwW9Pb24urVqzh+/HhF85T3O+44UKMPICmDdGiZ46rRXf0ZoLJHOR9KOxTAjhDnezlh3NB649bX11dE7HUkXUewjH+nMmOkwpgitbm5CavVKgWIaOxwGJ3j+2UYhSQA+P1+xONxTE9PS2tIjmKxiLGxMamtoOmcupAkCyzzd16HnZZKpZK0sSTowuifXjcCR1VVVZKvy/WtqqoS44URLypXdhLSxrHeE3pt6GDqAp4sBkinllRMh8MBl8tVUWiQTB220uQc3I2147W0Ya7PkRGx1g6yZq7xOYaHh2G1WuHz+SrAUr6X7Bo9fzzbZFXQUGS0iYJUOwd08mnk0ACmMqITYpQFwA7oRAVJ0E2vByPGNTU1GBwcxE9/+lMBiXWUh79rKjWdFILEev/cj4P7em5uTiIaZvN2C2UCrozukj5Nim82m5V0PRoaNNxo/G9ubsLj8SCfz8u52NraEvow54mFcAFIfjbXhfOrZTkBN76m9zDvgdfas2ePFGPThgpw/6U43W7sdp8ejwfRaBQLCwvYt29fRa0Zo5wsl7cLWXq9XkSjUVy4cEE61OmzyvVcWlrC0NAQLly4gE984hN4+umn4fV6AQANDQ1S0J/6kOvCfTE9PY233noL09PTOHDgQAX7gtGp8+fPo1AoyHeRrUA5QwalBgsoFxwOB+rq6gSooRy7H2qdvJ+hZerMzAycTif27dsnbCRgB9jmIOhdU1MjgSMNwtARZwSYepS1UHim6NQ7HA6h8dPZo+OozxYjzrFYrEK+6fvSaVPcE7z3QqGAubk5dHV14fDhw6itrYXf78cDDzwg7I/bRUjvxsjn87h69SomJibEuXv22Wdx+vRpjI+Pw2TaLhY5NDQEt9sNn8+H8fFxcSo499TvTG2gjUPWA9lM2mGio019xOKntGt4tug0EAiKxWI4deqUgDvt7e3CMqPTRhtUn1fKagaNaDsxKKaj+pSxpPZrx47PWC5vs5FGR0fR2dmJY8eOYWFhAZlMRhxVsnQmJiYQCATQ0NDwLsfrTg/qDeM1tD9A2UHnfHh4GPF4HF/60pdw4MABLC8vY3V1Vey3+vp6OBwOJBIJhMNh5HI5NDc3yzym0+mKfUy9try8jJ6eHglEkD21tbWFaDQq4Fcul0OxWMTo6KgwNpiSVV9fD5vNBofDITYNZR518NraGtLpNBwORwUIQR1eXV2NmzdvYnBwEH19fXj88cfx0ksvyZk1sps5d/p1zcYw+kl3Ymh2hwYq+Tftq/HvGxsbwrK02+0YGhpCbW0tenp6pL4kzw59LqvVitXVVQkM0K4kKBKLxZBOp9He3g6v1yssx6qqKhw4cEDm3mKxYGBgACdPnpSzfubMGZHFTHtj7ZNEIiFnnClqhUIBKysrKJfLEtQHIH4B9yhTFePxOK5fv44zZ87A4/FUgBNerxepVAqhUAgdHR3i83AYQTDaqB+Uf6ltYO7D69evS3YDALS1tWFjYwPxeFxkkNbn3MPa9tbBWC1LKLOMe5N7h8FD+iV8rbm5Wbp/0Wbl+/hTB5R5D2SbAjtpyLwe74/zDuyc2d2CuASFyuUyOjo6pDtiW1sb5ufnMTo6KnVdf5Nzd0eBGi0weAjZZ10fWv1e42bQApgK73YLpyvbRyIRaZ/c2NgIYPsg0rkwTi4Pltm8UxCXg0rNaIBoRanpxzzkXCjj9e61w2dEAjmoXPx+PzY2NpDJZLB3715pM1ksFrG6uop8Po+BgQFsbW1JAWBdv4CAHBFo5uHTINBAC5UcjXcaI8AOxZtRvqqqqop8cAppDQQRIafDrYsqAjtrxnthLQcKW94/38MuDqw2Pzc3h8OHD4uBxkgxALknXWfjgx63Q6ZvBywY6agajEwkErh58yZ8Pp8Yh+VyWQAsY5V0HU2mgONnmKNfXV2NhoYGOBwOpFIpQawZ0QAgdG0aGZlMRlgabE3MwXQYDRzRiSGwwLXl/vP5fEgkEkKl1Q6hkcWngRv981fN6b0cnO9isYipqSl0d3cL8wzYXm/dCY1OHj+jmVE0SJkSRXorARcaSZS1VqtVuo/wvJtM20Xj2e3C5XJVGBJcGy2DKPszmYwoW+41MubY3jQWi0knDCMV+H4eu503ABWGZlNTk8guMmMymYxQv8vlMkKhEKLRqKQfHDlyROZfp9n29vbi7bffRiAQwOrqKv71X/8V169fx4svvigdMzj3pN9XVW0XOhwfH8elS5cwOjqKRCKB3t5eNDc3CwALbBsyTU1NOHr0KPx+P0KhEJxOJ9LpNJxOJ8xms3Sb0qnNBO7MZrNEnAjoc17ux3N2u6FBcrZvfeihh9DQ0CB6kUA0ANGDuVxOUtk46EATeG1tbUU0GhWZqeth6LRh1sbj+S2VShWsGr6vpaUFfX19GBoaehcLgrpYO4zamaIdU1tbK+AvO4Oxc5hxb9+Lkc/nMT09LemdFotFulVGo1EcOnQI4XBYmLcE9KmzGTjiutAm1OAIgwHatuPabW5uwul0orW1FU6nE8FgUAqFAttr7Ha7ZU/Y7XY4nU6USiVh+tbV1UmhYd6n7vyUyWREX5bLZam/QaCuWNxuwKAZbrS7mLqsGcUayGfQaWhoCA8++CAsFgtmZmbQ29srBTqLxSLi8bjUgtDA+p0eWm5yaD9BX5epfeFwGCsrK2htbcWXvvQlVFVVYWJiAqlUClVVVQiFQpLCZLFYkEqlZO0ISNKx1kVoyWycnZ1Fb2+vsCBTqZQU3ibzur6+Hi6XSxhP4XAYVqsVXV1d4mCSNQG8u2YgWw4ToGdwi/ZwMpnE6uoqzp8/j97eXuzfvx/Hjx+XDppahmo5oOWwDtgZndA7NfhcRpmgZQuw46THYjGsra1Jetfa2hpOnz4tqblkz7hcLszPz8tzdHR0oKmpCQAQCASQTqfhdrtRXV2N/v5+rK6uIhqNorOzE8ViEYFAAB0dHSgUCkgkEnA6nRK0Yncvyg+edbJEisUirly5Il3weH6z2Szm5ubku3ieKL9phxEA5j72+/2IRCJob29HOp3G4uIiRkZG8Lu/+7vSlY3pVtpuMgKYu4Ead3otacNtbGxgdXUVs7OzOHLkiLCEzGazdK4D8C7yA4fWP2Sjaf9as3qBneY0/CwxATIatf8PQAK5DMzqNDFd2kDPH4P0mvjBsRsIpu0V/l2DZ7lcTjrBsfkMsA1mXbt2TVg1RizkvYwPpOuTnhTWqTECL3xQbl5Nxed3GVkb/G7S5PmdZEgAkB7vOoeekUGtAIzMAy4Soya8Nr/fWMeC30khzUNN6p529u61IWoUmrqGDPvNr62tYWtrS4rZ1dXVIZVKYXZ2VopFhcNhMby52cig2NzcRCqVktZoiUSionhiubzdgpJtKBsbGwXk4mEpl8si1LSTR3CEIIrJZJJ2qDSQgR3jUz+vpnQTRGBND+4jAJJzypoNRO+Z1sZaDbW1tYjFYjIfbBN4N4Y2jI2ouvHwGxWyPpfc36FQCKurqzhz5oysI9fBbrdLRzUKPv7NZrPBbrdLhyyeHSqjUqkkkar19XWh+nPoqEtV1XY6E9ue2u32ihxjgmuUAzReNLuiUChIocByuSyFIhcWFnD48GER4nrO+FlteO5m1NxLJ+R2g/O6urqKZ599Vozxt99+G+l0Gp2dnQC2QS/SLSnP2JZW0/O1YVddXQ273Y5MJiPnIZfLSfSXQBkVcj6fRzweFyUJVDIfNRhLgA2orGXFezGbzQgGg3C73WhubobP58PU1JREIu4Hx/C9jtvtHRokoVBIjEfqSTpF1Ic1NTXw+/2YmJhANptFe3s7HnjgAYn+66K+7e3tOH78OJaXl+WcXrx4EfPz8/jCF76Ap556SgDYqqrtmjNLS0t4/fXXcePGDSQSCZjNZjQ1NeHUqVNob2+vmG+mop4+fRpnz57F3NwcfD4fUqkUNjc3pTArrwHs0O11XTGjMaVl2IdhaLslHA5jfn4eX/3qV8Wh4hwQGKacoi2io4nawdra2oLT6UR7e7vULtDgAecvk8mIg8IoLeeYcoygte//auakUqmKe+EwRrv1fVHHb21tF9pkEKy7uxvBYFAA29sZ5ndrhEIhTE5Oin7o7e3FqVOnsL6+DrN5uw3y1atXsby8LGkRZP+SKeN0OoWJxFoujODTmWfgiHNIVkuhUIDP58MTTzyBAwcOYHV1FTdu3EAoFBKwhC15dcSX55YADtNMNS2fIMK+ffvQ3t6OSCSCa9euVYBHOsUcgNhUtG14djVzlTYcAGHAhUIhrKyswOVyYWVlBT09PWhra5Pi0cFgENFo9J6A5dpZ5eDeZBfO1dVVfPzjH8fAwACSySSuXLmCQCAgbYN7enrQ1NSElpYW2O12YbgxyMF5px1E+co0puHhYdhsNvT394ut39HRIUVJ8/k83G63sGcSiYS09CbrnnYwi1Dz2gymaHnAejYAsLi4KC1/A4GApKd98YtfxLe+9S2kUimxfe6lHNXyRe8To41KeySfz2NxcVECd/X19bJOBCNzuRxaW1vx+c9/Ht/+9rcRjUYF+GT6osvlQm9vL5LJJMLhMIaHh1EsFiUoUCxut/3+0z/9UzzyyCP43ve+JzVNyGSj/QFAgtCJRAIulwtHjx7FxMSEdAgDIKBaoVCoYDeyk5yWtwwE0z8pFosSmGRXo9XVVfzsZz/Dc889h2QyibW1NdmfOlCpAYTdzsWdGrRfNOtzfHy8gsHb3d2Nzc1NOBwOhMPhiqwT409tf1NmaSCGoIv2y/iMmvigS2jwvnTwBNhhkenXNKhFkI2Bd8oSfU/6uhrU1DadMQiez+cRDofhdDrR1dWFeDyOTCaD1tZW3Lx5E4uLi/B4PO96/vcy7ihQYzRMOSF6YoGd6Dxf48Jrqrw28riIVFBcAEYFzObtls908jg2NjaQy+Vgt9sRDocrHBdtQNFhMFKv9CbTThy7V9EgslgsyGaziMViaGtr+5Vzci+G3rAAKnJll5aWJLrjdruFabK5uQm/3498Pi9dOqhoAEiKUCaTwfLyMt555x3JEWR6imbLaLS/VCrBZrPB6/XC5/NJASYamzRStAIjaMBWhpriq1FSfU3+v1zeqYtBwcf7JHNA0yzpmALbOeXJZFIimA6HAzMzM6irq5MoFY3yu7WGZEZwGAW3fq8RcNAg6vT0NMxmM44cOSLKhYwKbQxSsfBsMXrO6A+ZKgRFg8GgGDA853wvv4MMnJqaGqytrUkuOQW5BmI5v1TyumYJFZ+u+2Cz2dDT04PJycl3gTo6rYvfrZWf0cC4H84vFSbvs1gsYn5+Hl6vF263G2azGWNjY1hZWUGxWJQURZ4l1kVgvS4N3jF6rKPowA4gls/nKwBxrgXXNZvNyhml8b/b3JGir9lyjIgyqj07O4tMJgOfz4etrS309PTg/PnzyGQycDqdsjd4pu/3cTtlzP3HdBRGbpubm8VpLJVKmJ2dFfDa4XCgv78fnZ2dFYCLTv89ePAg1tbW8Itf/ALA9houLCzg29/+NuLxOD7ykY/IuZ2amsKlS5cwMzMj8sRiseDkyZPo6+urqB9F585qtaKlpQVdXV1YWlrC1NQUfD4fMpkMgsEgOjo6JKWRz8+frFXGyCfPGaNr9/PQAR3u7WKxKF0ufD6fyJlyuSxFKtn5iWdHF+GnY0JGjMViQTKZRGNjI/r6+qR4q2YhsuCwx+MBsB2pdbvdCIVCEvCgnEin0/jlL38pIC7TH2l7cVAe0kjW30Fnqlzeji43NTXB5/NhYWEB2Wy2ol3zvXIQJycnMT8/L2tz9OhReL1eTE1NwWq1Ip1Ow+/3Y3l5GeFwWGpc5HI5dHV1iR4Ctu0iAh41NTViw2g2DtPXAEggJxgMIpFIIJ1Ow+Px4MSJE4hEIlhYWJBOfJSZZK7S9qBdQmYEQZZisYjGxkZ89KMfxeDgIKxWK4LBIK5fvy4OHAuFawastmspz6nr+K9YLIqtTBm+ubmJUCiE3t5e+P1+FItF9PX1YWpqCiaTCfF4HLOzs/j4xz/+rmDYnRzGYCpQmXKg7XvNOiwUCvjc5z4Hi8WCW7duwWw2o7+/X/QGC26TBUBGMPe61okE7CwWizj0LCbLIGOhUIDb7UY6nUZXVxfGxsakmGh7eztcLpew6pxOp4B2mUxGbF3qQ8041oWp6T+xqDGDiFevXsXg4CAOHToEq9UqZQn0vP269fkgQB0dHNyN5WN0eBOJBFKplKwRU5Goj8gI29jYwPHjx5HNZjE8PIyrV68KqLm1tYV9+/ZhbW0NS0tL4ht4vV6pJZXJZODxeHDt2jU8/fTT+PrXv45vfetb8Pv9cDgcYheSWZfNZlEoFLB//36EQiG89NJLWF9fR09PD1paWkTOE2BaXV1FS0uL6DSeRZZHcDgcUtCaaavUwzabDfl8HmfOnIHf7xeAKpFIyLXI8uHc8fzp1+700DoP2M4emJyclJSsrq4udHR0oFjcbte9W3CcMs+4F3fzU3gGdLCdZ0P/XeMD+uduQweleV3N8OZ9GMkgWu5o4EbbtRob0M9IkoLT6URzc7OA9FarFZcvX8b+/ftFBr2fcccZNfohWJdC10vQm4oPqR0BGuJMJaLSoqFBCigVjp4kGsD8SQVIR5FgDQu1UTgySszv0NEwCh8tzLXTwjQZAggcxsW8l8MowPmPnQHIeujv7xe6XSqVwvDwsBiXNpsNHR0dkueby+Vw/fp1zM3NYWVlpaJTEK9JY5XRWGAnyprL5SSPuLe3F77/S7+hAtX1NKi4aDSZzWbpBqMPnUa7+bwEHPg5KksOCkEqbwI5FBIbGxtYW1tDS0uL1MxhtI0GX0NDw12ps6AdZUaBjMrPKDh0Dju/o6qqSgRvS0uLUHbJsAqFQpKzrZF1CpdkMikdM7g2GnkuFAqYmppCZ2cn7HY7GhoakMlkEIvFYLPZ4Ha7YbfbUV1dLXuAjgvvmeeeZ0wbsroNMfcAAWEi5QcPHsRPf/pTKeTHfaf/cT6M/9fsIeYX3+2xG+jNsbGxgeHhYTz00EOSmnDp0iUsLi6is7NTIjh0sCKRCJqbm6XSvX5WoxPK80CgjOcRgKQqaUCPDh4VMyP6/L+OsnBeOcecV3YeWV5eRm9vLwKBAFpaWmSdV1dXxZi7X8CzXzWM0Sg96OwVCgXEYjHY7XY5gy6XS+i/i4uL2NzcxKFDhyRdguw9zYZi6iVbjT722GPIZDK4dOmSzHsikcD8/LykBQcCAdy4cQOTk5OShrq1tYWHH34Yhw4dArDDcuI+YJSLBR4DgQDeeeedCrlfKBTQ19cnn+EckFkVi8VQKBTgcrkAVBps90Odmvfi3PAna5i1tbWhqakJZrMZiURC0oh1rRidWk15wyAE9VNNTQ0WFxcRDAbR3NwshcG108M6PzT06cDxXAKoOLdbW1sIBAKw2Wyw2WwVRiflKvW9lgOUe7qWHJl4HR0dGBoawvr6Orxe7wfi8L2fsby8jEgkgqqq7S6eDzzwgKS3dHR0iH3GdELdNp3OMp01Fo/VaaBk9OoIOe1RAi/Ly8t46aWX8PTTT6O9vR3JZFLA5RMnTmBhYQGhUEjWkPPFhhTATkCJrNKenh50dnZiYGBAQIP29nbpVsTuJbSdtT7kdxJ0Ytoj7WpgW5ZzX9bX10u6u81mE7v7Ix/5CF577TWxxZaXlwVo/SDW3KibdfAWqKxRw7/n83lEIhHs3bsX+/btg8Vigd/vF3uC+16n/1KOUR/pNLOqqiph59bV1aG9vV2KpnLt+F4AePvtt/HKK69IF75sNguPxyMFZTOZjOjUxcVFzM7O4vjx4zJ/tDNZ34Q2Kp+ZDVnIcK+rq8Py8jIymQyam5sxODiItbU1AeloExqDs8Z5/iCGMcCtB0FBghi5XE5SZqxWKwKBAAqFArxeL9bX19HW1iaFlT0ejwQVWIuS711ZWcH4+LgwBxsaGqQQM+t6MdXx6tWr+OY3v4lvfOMb+PrXv46XX34Zt27dEvuFvo3NZsORI0dw7tw5zMzMSBCDdj7lv81mk3NNUIkZBS0tLcKSra+vF99qfn4ePp9PWouTmdnZ2QmbzYZkMonOzk6k02kkk0kJRlOX0p7XfuoHJX9NJpOUL5mZmcHMzAwef/xxmM1mtLW1wW63Y3V1Veqx0Aeg/jCCG8COf61tT74OVNbV5POSvak/w981IK2vzfOp9SJ/6uwMPTRzR9eUMwaN9fxoP5+BjUAgID6q3+8XVtjk5CSCwSB6e3srgj7vBSe448WEjeg9W3Rq5IuOpo508yeVCTtFcPKCwaBErxwOB3K5XEUOvQYIOILBoDgSzc3NyOVyGB0dFWqn0+msQP00kqYXja9xkzHyRGHf2NiIYDAobRONSOL9ANTo9A8azlTWfr8fFosFra2tUnzZbDZjZWUFIyMjQut0Op3Y3NzExMQELly4gLm5OXGcScEmTVgXfeU/RpZ0gVI69RsbGzh48KAcnurq7a4LxWJRWvLxkBCRZMSBRqmRqcVn5qFndyga0UBli1IeUO5BUmAXFxcl+sY8cxrjjDTqqM8HhXBr+uOvWmt9+LXS1vsgGo1ienoax48fr2BUrK+vC0hj7F5BBct1o6O9m/O/vr6OXC6HgwcPSvRgeXkZXq9XqtoHg0GEw2FJm9DygMK0UCjI2WNEiWtus9nk3nK5nLA7tra20NXVBb/fj6WlJXR0dFSkWhDk1QCMjgbdb4NrT0Xk9/slegtA0p2uX7+Ocrks+zadTiMYDKKrq0u+i4YpHTXjvPB6XHdGGxi5ZN0CzUrUoAT3HvcZwRqz2SxpUs3Nze8qlmg2m1FfX49AIIDFxUUMDAzAbrdj7969GB8fx4EDB2Td9N7mNe+3wTkx3pvFYsFnPvMZjI2N4c0335TistXV1YhEIkilUrhx4wbMZjOeeuopuFwuibTy8wQudQobAWa73Y5nn30WuVwOV65cEXm8traGUCiEubk5idilUikxUp944gk89thjkgNO54KddMj4CAQC0tmoqqoK77zzDtra2qT2EWs1cLD1ODuU9fT0VKQ/a6D0XuvJ9zK474rFIqanp+Hz+VBXV4d0Oo2lpSUkk0nU1tYKC1inTNMh08aeBqgSiQQcDoek5gCVxUBtNpvYMeymA9vmZwAAIABJREFUpYsEa0cSgOTMA5D6JbSxtLOpI460DzSwzd+rq6vhdrsFiDCOe7GGZBMB2/PT19dXoZ9Yi4bGM3UIi5cyradcLksr7mQyiVQqJc9MQIXfSbuPgHhXVxeSySTOnTuHI0eOoLu7G6FQCMFgEB6PBwMDA3C5XBgeHkYikRDjn3YwbYmWlha43W5hfjc1NSEUCmFxcREmkwnDw8MIBAJwOp0V3U2ZUqXTaJiqrSn9BJ6oT2tqamSvss7LM888g+7ubqTTaXR3d8v5pQ0ei8UqWtDe7aEBRc7fwsICTp06BZfLhWQyiWg0CofDIfdIGcP6M9wHrCml2Z4EN61WK3p7e+W6zc3NAuwwlSaRSODKlSuYmZnBoUOHMDg4CGA7kFUul0W+1tTUIBwO47XXXhMGrJHpBOwUFiaYVFVVhcbGRjz11FPCAi8UCiKHe3t7cejQIYyMjFQ4p/dqGG1PbbNqfV1Vtd0lKRKJSNkIAGhvbwewvcaNjY0YGxtDb28vxsfH8R//8R9IpVIV9bn4zKVSSWpo5vN5zMzMSOCXXTAb/j91bxYb532djT8zQw45C2dfuYmbSVGbJduyLNlO4jjpksRJgCBIUvSyBXrTuyLoXW9y24veFshFkLYBEhhGNqVI2niJY8dSJYtSJFKiuJOz7zs5nJn/xXzP4ZlXtOP+P9Hi9wMIUZzlfd/fcpbnPOcctxtmsxnXr1/H3//93+O73/0uvvGNb8DlcuHq1avY3t6GxWLB5cuX8corr+AHP/gB7ty5I846mVQOh0Nkn8PhQCgUkto6tEe576amprC4uCj1vVhCIZvN4tq1azh//jzMZjMSiQQ6nW7jANpFTqcThUIBwWCwR6YbGcX8/1GNvr5uZ7hr167J83s8Hvj9fjSbTalfxvXXrHuupbaBtI1ptLk1gEO7kwAMdZBRf/J1Xl/vM+3/aHIHgEfqxfH61IusF6ZBGg3IGJ9Ng1G0yzhX5XIZfX19Ug9wfHz8kbP/J9fhE73rEw5t4NNIIZpPZFPXpOEDcpJ0ygsjhWRA9PX1CZ2Uwo+RJ+OEa6fS7/cjm81KtGRkZASFQgFbW1twOp09UWa9AHpDabSO7yMNGehuykqlgnK5fCyp3EbAiABaOp0WqpbH44HH4xHmUTwex4cffoh33nkH9+7dkwNEJLher8Pr9Qq6zCgMabR0HjQKur+/36MsdXvtlZUVmM1mKQRIdJIRXx4sk8kkBb4ovJhnzfeaTCYp6khjhAeQwoRKmRRiKmA+j86ZJJCgGVuNRgPRaFRof0dtpGq0WgOeFGDagOH7P+q+Wq0WVlZWEI/HMT8/L5+hoaeFI88r15LzYqQk8kwTkAO60aLNzU0pwMbWy3QU4/G4CLV2uy1FZLUgZrcG7jG2PWTtK94X2UBkWoVCIQDd/O4XXnhB5o7ffVgE3wiGaaH/JAfnl47YnTt3JL2pWCxieXkZVqsVn/vc5xCPx3H//n1x/s1ms6T06agM6xYQ2OSaci41E4BRP3aQYhqAMRqvo8LAgeGpI79skTo4OIhqtSp1I9g2l/c/MzMjbaCvX7+OdDqNYDDY4/geR4CGw2i0cqTTafzHf/wHAEi6bH9/P/x+v7AMHQ4HIpGIBCJ0IeBWqyU1wRqNhhSpdDgcwui02+348pe/jO3tbdy7dw+VSgWrq6t44YUXMDU1JVRwBj/m5+clUqaNjFarhXw+L+A0ZSnBpUqlglAohOHhYUxMTKBWq6FWq0n6E5k61WoVN27cwNLSEubn53t0wsfJqeM4qD+Y8vvss88KC4rttNnCt91uCzgCHMhKDeJQFwKQOm9kHg8MDEg9H8omtuMtlUooFAo9hWcBCNsTgHSX4us0RPVcayNZp9FRnpNJRQYJ20rzrOvI4JMY8XhcnAOmgjElW9ch0TKdUVgWuydIzMAgv4tsQso8OtF8Vu5x1qZotVp49913cebMGYyNjWF7exuFQgH7+/s4efIkBgYG8IMf/ADxeFwY3TabDRcuXMCrr76KSCSCzc1NiVI/fPhQALabN29iY2MD4XAY7XZbmjownY0OKZ+9UCiIo0/5wftmfTDuh729PZRKJRSLRZhMJgwNDYmM8Pl80lgilUqhWq32gH2f9tA+BsGjnZ0dfOUrXwHQZWdWq1WpG0IWPvBoxJyOIc8EZV+r1cIPf/hDXLx4UWp7MWWXMq7RaGBnZwf5fB7f/e53BTxnpyDqXIul2+Xr5z//uXT6I5NOg7B0xnXgmal73HPUmVtbW9jc3MRTTz2F+fl5vPnmm6hWq1Ijjt/3aQ9toxJA0KCwZqOQaTA8PCyF6Vmzh35fMBhENBrF0tJST4dPnlc2qvF4PLDb7XjppZfwn//5nzCZTAJ0EEjd39+XM5xMJvHP//zP+N73vocvfOELKJVK+Nd//Vc8//zz+Lu/+ztcvXoV169fl5Rjj8cjbHoAAqZQD7PYM9lmdrsdmUxGgNFqtYrvf//70pZ9b28PoVAIL774onRF1rKGrNN4PI5isdjjZwK9NcW0/njcg9fJZDJYXFwUJkg0GsXg4KCwR41BF6CXya/3ogZmjIFkzZzj/qGMMu4nDWgeptd4/zxXOgihzx33Kge//6PsEn2PRp+Ln2VxarvdjuHhYSSTSTQaDQQCASwtLeHll1+G1+vtOft/ahzJCnPR6Nzm83nJ59N0ej0ZACSvr6+vTxRUq9WC3+9HIpFAoVBAJpNBKBTCxMSEtAEGDtKedASBoILf75f0J0YdubkLhYJQS3X02Egr09+rDRkOpos0m0050MfN+OQ8s8MTiwLu7e1Jl4LFxUW8/vrrUrSsr68Po6OjyOfzyOfzsNvt0nKbABWRbA6tTLlpOW804ukU0miq1+tYXFyE0+nE6OioOCjAQSoMDRGyY8ig0PR8oyOnjTOjMTo4OCgKlfReDXQQ6NFOLiNehUJBnvvjaKaPe+2MRj/nBzjISzcKACOiXa/XsbCwALPZjMnJyZ7Cdoy00Ykvl8sS8WNkgQqPxo4RnGUe6N7eHrLZLEqlkgBtjBbTgNVnhWsIQBQamTNcb4IG3ENsXcmImWbfsNZDvV6X7jqcA703NFtFR4OOy/nVAFI+n8f6+jquXLmCvb09xONxABDQeWpqCtvb20gkEiiVSpidncXk5KSkJGlQRj+nkWJO5aXTwDjPNE645tyLej/q88drWywWqeFF44PFGyuVioC/drsdKysrmJ2dhcVigdfrxdbWFgKBgBhqRiP3uIzDQCStyJeXl9Fut4WduL6+joGBAVy6dAnValU6irDoHGUTjQDdnaTT6aaXUp/Z7XYUi0WYzd0Ci1/72tek3a7NZoPL5cLk5CTa7Tay2Szu37+P/v5+vPLKKyIH6dRzX+gipYODgwiHwxgbG5NIdDabFXCXNamazSZ2dnbEwdva2kKpVJKWpAAeOV/HHXTTEeJWqyVBmeHhYQwMDPR08CFwo41A7RyS1abP3uDgIIaGhmR/NxoNcQ4dDkdPsdFOpyNRaBaAJ7BK45WAmtl8UG9DpwZrY18DtkDXlmJNuE7noLYVnSg6pUAvm/JJDLKYAcDj8YijQ/nWarUkHaJQKEiqN+dqf39fGgxwUK+YTCZJk6ItUq/Xkc1m4Xa7xVkADpoRjIyM4MGDB1Jkv91uIxaLod1u48/+7M/w13/917h37x7OnTuH+/fvw2Kx4Fvf+hby+TzeffddiboSdAKAO3fuYG1tDcFgUO5Zp29RPnCNdXercrkMoDcdk3YXcCCHyXrnKBQKOHfuHMLhMNbX12EymZBOp2XfHaXM1fJS22NG56jT6SCTyWBgYAAzMzMAIEVNtfOm7b6+vm5RfA3KcT4ZiLJYLGg0GnjzzTd70s8oY61WK3K5HMxmM/7mb/4GX//611EoFPDw4UOpXaNZJKlUCiaTSdheDCrSidSBGL7OZ2CwknYvW38z3YSA1Pb2NoLBIIDeQuX8bv4deLRm5eMalB+8LuWbZiwBB2eIut5ms0nXJ5PJhNnZWQBdufT6669jaWkJyWRSgOJqtSp1JE2mLltuc3NT2ODT09NyHYJsDAwzYJhIJPDGG2/gH//xH3HlyhX87Gc/w7e+9S34fD4sLCyIv2qz2eDz+aSGZaFQkHVhbdJarYZoNIqxsTE8ePAAxWJR5CZ1NEFP1npiILHT6cDj8SCXywkg3Gq1BHAmO8xut/ecBX0ejhIwZcaD2WyWrmlutxv7+/ui+zXBQZ/RjxvGvch9arRB9d/09/I8MmjP/UZGMNCVaboDJc8cWcTaXtWEDP03DXAbmTRGYgd9TAZcfD4f/H6/FJ13u91YXFxELBYThtcnHY8dqNFKmwg1J0tvXB2JpaFRLBYl/5eHV7Mxstms0OGJHvOzNBB5fTpt2nFn7i4/x/zlQqEgjqQutsfP8W96MUmfLBaLIiA/Laf9fzsoLPXvhUIBtVoNhUIBbrcb8/PziEQiCAaDePjwIW7fvi1drGi0sJMPFV+1Wn3EKNLUfDraBLVIJyNTRVP2CDKsra3B7XZLYVm+p7+/X9gUFKIEF6jQ9QHU+4vGDZ+d90nDhoqEPxQSTAFrNpvI5XLwer3irGxvb0ueLCPbQG/U5iiHdt41+0W/rh1Zvm42m5HJZHDr1i1MTU0JK4Lzw6J5NDAsFotEJImeE9TU4BzPFyPGBGqArhE7NDQEm82GarWKRCLRwwoCDgABbcDQcGKFfIJFrI/CAuI6F59OZqPRwPz8PG7duoVyuSzFv+n4aIDGCNzQoD0OdGKgt4gpKbrRaBRmsxnZbFaAEEbqnnrqKQSDQdy8eVOexVgdXxtQWgHyd+4BHSHTYNru7q6AaHoNuY8O2/9675BOT8ZAOBzG3NycdGMpFArY2NjA5OQkRkdHsbKyIh28eD8aYDsug+ukI6OtVks64KXTaXz5y1+W4pYPHz7EvXv38Otf/1r0nslkEqNIsym4Hpx/zjUBFrvdLnt8b28Pk5OT+Pa3v43r16/j4sWLmJ2dxfb2NsbHx3H+/Hmsr6/D6XRiZGREWFasK9dudztR0fDQLCx2hHI6nVLEMZ1OSxpFMpnErVu3ZI/Y7XYEAgFEo9Eeh5jzRR3+SY28JzGMzg7rwni9XtGnwKMA1N7enoDfWkbrKB/QZWdQ19LYpMNNY5Qdifjd1F8Eieic6vobXDOjnNXPZQxmUP+x1brH45EINfV7sVgE0Gu8PonBNKFyuSwpOdpJr9VqsNvtOHnyJO7evduzr3nemM7J5yFzjDYhWSR7e3tC9ed8slio2WyWlAjqOQ2e3b59G+FwGIFAAIFAAMPDw7BYLPB4PKjValhYWJDua81mE36/H3t7e1heXkaxWITb7RZbDYB08qK8oFzWMptBz0ajIQwhMlC1U8L9Rr3K77fb7Zifn8f//M//CKOGhYaPmk3zUfpD21Y8d6x5ZzJ1u5fmcjkBQlk4mbKUNoQxrYH6sa+vD16vF8899xwKhYLUHNGMap5D2qkPHz4E0C0azDQ52pjlchmlUglXrlxBOp1GOByG3W5/hJGkgUMdJGZ6Hm0vrjHTZQYGBuD3+xGLxXDhwoVH5uvj5vZxB6J0oPywa1J3cY+73W588MEHMidXr17FZz/7WVmrt956C2+88YYAa/QlvV6vAOXtdlvqRLZa3a61AwMDwm4kKEQfjanzlUoF9+7dw+7uLgKBgKSLa3DabrcjFAohEAj0nGWux+7urtS/2tjYgN/vlw65Y2NjKJfLuHv3LkKhEGw2G9bX11GpVPDss8/iqaeeEnDN7XYLG5bM8EqlArfbjXw+/0gQ7ePW9nEMvS8qlQref/99sTFY1Lper0uDEN4XwTgd2CB7lP/nueNZ4tBsbO5/bfvwmbXMGh4exu7uLtLpNADA7/dLdsTOzo4Es/hMxufTgIsOfOuACK/H343+Pf9P5iyvk0qlMDo6CpvNhlAohEKhgHA4jLt370pq//+GCXU0nKn/M2w2G4LBoETPNe1JPzgXNJ/PS7FRl8vVAwroegq6q4ReCEY4WMSWeaWJREJykilkeSAZqWo0GhgeHkYulxMDmNEtvaF4PVLxms0mwuEwnnvuOQC9LIbj4kAYN8Tu7i4ymQyq1SpisRiCwSAcDoekS5w9exZjY2NYX1+Hy+WSImaaAgocbGgWciVYQkWl15sGKlMfWGCYAoDR21QqhVQqJZRdAD2UUNJ2Nf3NKLy08anZATTetFLm91Axms1mAV04Wq0WNjc34XQ6MTk5iXA4LEKn0+lgZ2dHoij62o9zaIEF9LaG47xwHglC6v1H4ccIXz6fx5UrV9BsNuFwOARwY8SBjCEAwoKhceHxeJBOp8Wp01ErrieBHJ0KSCG6vb0Nj8fTI8D5DLwGBTcNYCpvgjHNZlMKjnPtaChwv9dqNaTTaeTzeSl8yToaHzUoj4zA15MenJONjQ1MTU3Bbrfj4cOHUpyOUYSBgQFUq1V4vV6Mjo7CarWiWCwKG0OfBw4jGMW55xmmjCwWi1IkkfLTqEz/FLjF9SUlWO8Xt9stBUA3NzextLSEiYkJjI6O4uHDh9LBT1/juMjYjxtmc7fQaSKRgMvlEsDXbDbj6aefhs/nw61bt5DP55FIJIQ6Tf3F9wIH0UruZZOpW+iVaTJ07nkeLl++jLm5OZFZv/rVrzA0NIRvfOMbePnllxEMBuUM69pdxWJRUiE00Mf1YkrJyZMnYbFY8Itf/EIcIN2B7eTJkzhz5gyq1SreffddAeCNzsJxBWiAXlCc55DOm9vthslk6mFjahnc398v4Bn3PtmoOkLKOTWCN5x7Ri59Pp9E42w2G5aXlyXNRae4aMaE1hOcZ20DaYCVMljfA4vwWiwWkfkEO46Kdv9JR7vdlkAg04EI1pChTbkVDocRj8dlHtlxjvYIO/XQaSPzm+eqVqsJQMJ5IGDFLpTpdFrANjreQJf587vf/Q7nz5+XgFIkEoHFYsGDBw9w8+ZNrK+vY3BwULoKZbNZqf3EtAquo91uRyKRENlA+1gHqrj/ODgfFku3KLROmQIgAK8G7CKRyCN21lEGL/638rzVakkdr06ng2QyiVgshng8jnA43BPsZa1DABIsYHocbTm73Y6JiQmYTF0GEVmKTFcZGhoSJjfXSHeSpcNmNpuxubmJRqMh6Tm0s2nDaMcUQI+dyrnodA5S8SuVitQIi0QiEhCIRqPIZDJPPLB0GAOKz8B729/fF3nFWlEEYF577TXMzc2hWq1KhgLrpU1OTgojh4FDNqcIBAJIpVJoNBoYHR0V1gTXhHuZgAwBTJ5jnmvWj5qcnMTbb7+NsbExnDhxAmazWUpFEKBptVpSWuPEiRPY3NzEw4cPcfnyZeRyOdy4cQP5fF7WfnFxUVh3lUoF//Zv/4YXX3wRZ86cwcjICAYHB1EqlcQ2XVlZwfT0tMhkPTTgARwdQ6rT6dYlvHPnDk6cOAGTySSF83WJC76XQW8d/DSCwto/0na2DtbovaSBZx24ByA2SKfTkQLgrBtKfaz9QK2fjb4RcHDuDmPuHWanaF3KvU37iOzYgYEBBINBbG5uotnstjJfX1+XtNVP6iseSTFhChCHwwGn04lqtSopTwRnCLZoVg3RbxoxnEA6fa1WC9FoVAwaoLcTDt/vdrvhcrnQbnfTnAYHB9FoNFAul4U+yM3fbDYRiUSEjhYMBpFOp2XTaXQPOFAkdOaJtOZyOUxNTYli1GyGJz0Oc8wqlYrQOL1eb09XlYGBAUQiEUz8nza5dIw1y6TRaAggRiOPr2mFRWNBp0vwd34nDVkajtlsVlohcg34XuCgm5OuqUAGwGEHi4edv+u1ocIgJVbnPtIAtFi63Yni8Ti+/OUvC12zUqlgZGREnCO9Tx63wuTc6OfS4IRGovXQqDFZMXfu3EG1WsWZM2ekPSDnEICkCXIeAIij5nK5pNUuWwnz+qw9QvaTzl+lEUhHVUdfSEvV+4PPwWLCNKh0NXYdNabRzRo4ZrMZZ86cwS9/+Uusra3h5MmTPQ4v0Jvry7ni2mkD49MYf8pA5RlIJBJ4+eWXsbe3h2QyiVAo1JMyxvmhrGw2m7h3754AybyWZi0ZFZA2FOm4Wa3WnpbqPH+Mkml212HAtlZqTOvQxaLp9DJlY2JiQmjENG7T6TQ8Hs8jKRzHeei0RA0oEqhm/atIJIK1tTV88MEHqNfr+OMf/4jp6WlEo9EeAEUzNpgCxuL7lEt0qoHuviFIyZo1Q0ND+NKXvoSxsTE5R4zM0zHQlGbg0dRLMuSsVivGxsYEjHruuecQDAaRz+dRLpdx+vRpkeOkBBu/S8uy4zj0HqODx/pYAIRtpNdIMw653mR/MODEdTRGEo0BLb5Wr9eRSCRw4sQJSUFNp9M9QLc2NvWe4+sMtFDmajlMYIHp0JpxyudkzY9KpSJMjSfJqiFwBgDhcFgAYNodAwMDiMVi2NjYQCaTEYZXtVqFydRNpdCFsJnWDHRB7/HxcUlRTKfTApBZrVYJFGhQv9PpiL0JHNSzyOVywlC+dOmS1IhaW1vD7du3EY/H0Wg0hLmkWbJ07Jl2EQwGBUDL5/MYHR0VUInrTzt0eHgYDocD8Xhc7HCd5k9Zoe03sqWZRqnXl7b5UenFw+S5duqMjlKtVoPP55Nzw+5k9CP4WQZpAQjoxpRN3fmT3zs8PCwMLdakYQt2Ns4g4MrGJuziw/tijRxel+2iCYozkEUbk+um7c56vY5SqYR0Oo2NjQ0MDw/j+eefx+7uLorFIjweD0ZGRvDee+8J60Q7f1w/bQMe1TllkMzIiuCc6FRA1hc9efIkrFYrKpWK+F5utxt/+MMfcObMGSSTSSwuLopdabPZhNlSKpUknYwdI9nVkHVuKNN41vv7+xEIBLCysiK6aXFxEZlMBsvLy/jCF76AiYkJsVXJNnM6nahUKigUCujr6xP5S//HZOqyuX7zm98gEAggEolgeHhYGFwc/f39yGQy8ns4HBZ2FlmpBKII8rPostb9GoTS5TYex+D+aDabWFtbg9lslq5VrEtqZO/pQK22sfldtPN4jgmma6BSn3EdzNHfx+vwx2QyCeMqmUwiHA73PMthtgb/r3UrQSGOw4AdI9ikmW8a2yCQS/3ItGbK/52dHSnH8EnP4pGFQ0wmk7QSJV2TVfK5mDpyQ0WhFR+RTLZWHBkZkVx6Ti6HnkzN5uDvujixjgRxc0SjURSLRWmTRlqXcSJ571ro7u/vY2dnB6dOnXpkkx4HR8KoVMmW4HqwmrdW3na7HdFoVPKriRjSiSfopRlHAwMDYvwRVWTHDypDnUKho4WkqdlsNuTzeeRyOUEc6eTTCdT7iMaDjnbqQUGmlb1+ja9zPgAIQME9xfamzHFnd4ZyuYxEIiHOEeugHKXByj2phZsW3rxvzjPQWzMkl8vhww8/hMViEUeNSD/QjaRns1kRrNwTTDPTHT+oLKioaChw8KywjgDz22dnZxGLxZDNZoXloR1PPhcZXLx3TaPU60qQjcwrAkSsv/PgwQN8/vOfl5amRgdRC1zt7FCZPOnBtWUUm3RrMqDoFGpAEug60wsLC3ImtfFm/H79un5mHfmnYUqARp9BHTkx7kfjNehEbW1todlsSjqMTtvodLpF6zY2NnD27Fn4fD7E43FMTU3JvR0H2fpxQ+sPBidMpm4hWLLfPB4PotEoGo0GPB4PrFYrNjY2EIvFMDMzI86yUd5xDblXCXTTwKOjyDktl8u4fv06EokEZmZmJG+aepdrQ2eeYCkBCJ4TbRhy/nn22C4WgHSrIGDPyCnluU5DPmy+Po3xv9k/RkOPhbXpgLAbnmbScJ2oF/X5YPoabQnOLXUn5xlAz/qT6m20UfjDNdOBK+paMiioa9nqGzgA4mkXtVotiZoODw/D7XYLc5b3oQMnT+osarCLe0wHf9h1I5lMSr28YrEIq9WKmZkZYYfu7u6iVqvJGu7v72N6ehojIyN48803sbKyIvvZbDZLmjvlFYMXjNLTaeV+535JJBIoFouStvPhhx9ia2tL0qd59rh/WJuC9gcDEizwzQ6NdPrp0LpcLlitVjidTmk1zdbeOj2V1wPQs0edTicsFosE8Li3uP+PomGGMXAC9OoNLR+o8/L5vLAwWZPC4XBI3SG73S7FYAm60Nar1WrS+ZXgCB0/Bg80GMnzurm5iVgsBgA4d+4crl+/jtOnT+O///u/8eyzzwrzievIc6KZO9w/dDZp3zIIxfNktVoRCAQwNDSEsbExsYmYesJyBfl8XmoBGoORWnYfZcqace34f+3Ms+AydU6n0xE22tDQkBTaDoVCGB8fx9jYmABk1WoVGxsbPYA2WVG6jgudY64nG6Ww9ilrpV6+fBmNRgNvvfUWKpUK7ty5g52dHUml0UwWpkIxCM1UVKfTif39fak7EolEBFDnnpybm8Pk5KQwcegP5fN53L9/H+fPn5f0LSNrmOxN4MAW41xqm/hxj06ng0qlggcPHmBoaEg6GFHvazbmYTpbAy60qbX9afT9jdfm+w4DbYgJsL4swTKWAXC5XHJ2+X0ac+DftM4FIGAt33+Yb8V7oK+p/U0N3rA7G+WO1+tFJpOB2WxGMplEPp8XdvEnGUfKWyWi/ODBAxFMBG00vZ7GJYvy0XmrVquoVqs9BfX0ouqF5t8Pc6y008oJ144Jc3eZh6tf1z+8VxoC2ilhRylGjEmv83g8RznFn2jwIPPAEPigQ0shRiXQbncLQDM6QeOT+ZN0KoaGhpDP5yWaw4NAo8/pdEo7TConzhcRYm2wUvkzj5VtXzXrgo4EUVS9jjxomo7Nw6aNXToJLGDK92vaHQAB9zgHk5OTCIVCgoTfv38f29vbiEajcih5yB+34XqYk81rWCwWQdT1c2rDh8Z/LBbD+vo6otGo5NbriHcmk0E2mxU2mk4Po4GjBZxOg9PsKeDAmXG73ZKewTSkcrmMdDothozeN0aJYvyPAAAgAElEQVTQlcqXz0WniGwEovs66kAQw2q14uHDh8jlcohGoz0Olz6/3CM68sS5fdKD96SLWKZSKdnHjO5o4IzRHkaQSJ0n6KqVoBGoAXpTmBi9TaVSGBoakiKXwKPzqMHCj2JM8Exz3xFA1+9lJ6Tt7W10Oh0EAgFsb2/LfBi/+zgNrYt0FObGjRu4f/8+7HY7RkZGhAEQDodFBgeDQaRSKXQ6HVlrOhdU+nx+7nMCinS6aATxPhgBJGBK+r9mgfCMM7qpjRVeg9/Puh88J6urq8hms5iYmBDnhPdqMpnk/jOZjKSGXLlyRaJzvNfjuJbA4WA4W25qZgqddMopsk8044JMMrLJdMcmvs/IUOF1CUTH43E4nU7pAEXjmU6mThPXsppgBs8e5QTTrhipJmhPo5MMDtbBoZ2g7/VJDaYca71AVh5TZsPhsDhUlKETExNwOp3SIIHDarViamoKU1NTsNlsuHr1Ku7evSup33QqeQ50cVOmarfbbanX5HK5hO1G/be8vIxQKITFxUVsbm5KOgX3E3Uu57bdbktaBhkWbOhQLpexvb3dUySXNVn29vawsbEB4MCW8fv9GB8fF+BncXFROruRrUjKPkEDDRATrH9Sg/tc2wKUiaVSCWazWdoc7+7uCjMGgIDP8XhcCryykxKZWEzJ5lmj3cRIOe2OcDiMn/3sZ5Lmsrm5Cbvd3tPNhTK10zmon8gzw1bbOu2UrC7uM9q6BNzoW9AuZ9cyh8MBAPJ37dwaA9pHOfS1tczTa5fL5VCr1RAOhwXMJHiTz+cRiURQLpdx8eJFYbDwvA0PD+PChQvo7+/H66+/jkKhgGw2K/UKGRhmwJTnb3h4GIODg8hkMsL4AYB79+5hZGQEv//97zEzM4P9/X2sra3hl7/8JUKhEEwmk9Snos1fKBQEkKWcBLr1UZaXl7G5uQmLxYJQKITLly/jrbfeku5T29vbwtIplUr4+c9/jjfffBPf+973JFjOlB2LpdvRk/W3ms2mBEuBR3XS4xzcL+wwFg6HMTAwgEAgIHZANptFrVZ7JIWXdokx6KeDoUaChdFeND6PPvN8vd1uY2NjQwBqsr1ZpqOvr0/0qwYF+X2HBYU0cMo9zOtqVinPKeUgP6efjwA+C1n7/X5sbm5KwJHg2ycdR5b6BHTzaF0ul3R+8fv9ku9LY5CTQVAjm80il8v1RKc4KVr4aKNOb16NfOnByT5MydCA0fl1/Ls2lvQ1ksmkIJ90JCKRCFKpFG7evIn79+9jYmICX/nKV0SBP8nBZyGtlalkAGSTc3OSBUNHgwY358/r9YpBSCeRn+e6MdVNHzCuHYUo36cdG2346s9oJcdByrIWWEZanKZg6ign75UOrY5E0simcQagB0giBbNWq4lwpROkHfujAmuA3vQUPVf62kZhtL+/j9XVVaRSKVy+fLnHSaMy0OmBRudER9+43nTSCaZpgUZQ7OHDh8LayeVyUhSRUV7jffK+CAJxXYygIcE/Rj5YWFA7mF6vV4oCkhapUxW0IaMBDDqqx2HwPjY2NjAxMYF2uy1tU7mfmZ+rgYH9/X1RXDQgeZ40i0LLNn3muS67u7sol8sCYns8Hjkv/Jxm2GhGo9FQ5H6pVCpSoJuplHwWGsZ8Fu6Xhw8f9rCqjuvQ+4dzur29LZHXdrtbRHhzcxMXLlyQehd9fX0Ih8P44x//iIcPH2JhYQEvvviigB90Qrl/tYxmlJ9y13h2BwYGMDo6CrvdjlqtJkY90wQ1yKplKVuU8hq8F6YCJJNJ3LhxAxaLBYlEQoxtfle73ZbIHNmbiUQC8XhcZIBRxx63QbmuAX/KO7Iq2IlFAwVknlAWMoWzVqsJmEXWigaKgYNOTLQdeJ4597RZqJsJ+PBzNBL5vZxn3vv09DTi8biwIAl289yTEVsulxGLxXD27FkBHxgl1c6C/v3THNFotCdQUywW4fP5MDIyIufA6/Vibm4O6+vr0sGlVqsJQKHTeNPpNDY3N+VMEOCOx+Mwmw9qtlD/xuPxnlobBFZNJlNPBypd5DubzSKZTGJ9fR2pVEqAEa17yF4mIAYc6FzaG319fZLWZDabMTs7KzU6dnd3kcvlxKlsNBrCTF9bWxO2ELth0Vmu1WrY2NiAz+eT4CnrsTCo83G29P/N0A7fRw3tDGpmfKfTTZl3OBwCAOuC0JRDsVgMlUoF0WhU7Hd2syIoopsSWK3WntbXPO+dTgd//ud/jt/+9rfI5XI4ffo0Ll68+AjrVIM2DGzR5mSEncV02+02UqkUBgYGJBCqiwkTmKpUKtja2hKbaHBwUIAIdjw6zCY87P+Pc9A+o87XgDFZZalUStLvyKiuVCoYGhoSufb+++9jeXkZtVoNiUQCDocDo6OjAICRkRHcvHkTsVgMQ0ND0giGPoxmOzQaDUSjUQwPD6NUKoltazKZcPLkSfzud7+TfUww6Cc/+QlCoRCi0SgGBgYkvZFBE55p+i9M5fV4PDhx4gQqlQpqtRqy2SyuXr0qhcFXVlYwMTEBn8+HVColMiOfz0s9JaY+Mm3ObDYjFAoJw09nJAAHduHjPofUH8lkErlcDk8//TTMZrMAggSWAYg9pgN/HEYbWoNk+lq0K/hM+txoX18HnrUPaTJ1u6GazWak02ksLy8LU87oe+p70vaR/n7+6DnW/g7v1wiWaXu6VqtJeQy2bCejn6mM1OefRGcemSfCSM/o6CjMZrMojVar2y6RxfW0UzU4OCgREhZ404wGIo0AHvndZDpItdIHlv8S7SYCaNxQRkSMQlo7NFwkzWDge+fm5lCv17G5uQmv14v5+fknGnnQQ88dDUWge+8E07gh2+22pLfwEOoCWjTugV6qGFsK0iCp1WrSLo9gina4tUIEIJtfU491jRQdbeQa6X1BsE/Xz+C16Bxq45WFqHREk0AEFTydHzJ3dJTLYrEIG2dvb0+AGo0MP87B/WoEEg+7nkaO9fxVq1WhcM/PzwsgxvVhVFDPDSPCOkWNUVW9dpx/Rl11tAiAFPwDgEwmg0KhIAalBoK0YOZ5ZMSTz8o1o5OqO5GwQ1l/f7/Uz8jlcojH448UnjY6Gfr/T9p51PfBvZ/JZBAOh5HL5QAcKDENTvKcAV3FSBoxHXzjc2rnjjKOIKX+HuZL6zPJ9+qCpfoc8jn4Oda/ACDywev1AoBQ+fX6kNacSCSE5q87ruh1O6pz9/9nHGa0rK6uYnp6GhMTE5idncXnPvc5+Hw+3L9/H6VSSYrYDw0NIRqN4plnnsHS0hLefvttYS3SONCgOGWmZj/xR6catVotzM7OYmJiAtPT08I+Y6Sfc0p5ye+iHOT6EZTf2dmRFA92P6lWq6hUKuLQV6tVJJNJLCwswGw24+LFi5icnITX6+0xiIxz9WkNfcb+lHPIswE8mjPPIpbAQVtPABL9plxjXTe73Y7JyUk4nU6ZAzJtqKfonFO+aT0FdMFLOkPabqEO0meVLAkNBDHVAICkUpTLZVSrVUlx4r6gTKe+MNLDgU8/bY1DM3br9TpMJpPoAHYtjEQiQttngIXpTtvb27hx4wYWFxfx4MEDxGIxxGIxfPDBB7h9+zZsNhsikQj8fr+sI0EqANjZ2cHm5iYAiF0zODgoupIsDjp3ZCeur69jZ2dHdBX3AoNomiFJ/UzgfXNzE5VKBRZLtw7k4OAgarUadnd3cenSJZw4cUKAtmAwKEEX1tJjnbrf/e53KBQKmJubw+zsLM6fPy/pWdyTExMTeOmll8T2PYqUJw46ojpNwsgy4/nga5xzAKJb2GmF9j4/l81mUSgUpC00bZpOp4Pl5WU5GzoVn0AmAWueRYfDgZGREfzlX/4lpqamcPr0abGZmCbjcDgkuq/lAs/l9vY2Hjx4gK2tLQHe7HY7ms0mVldXEYvF5Fn5HazNUigUhGXDUalUeuYGeJTNqh3Vo2DbUCbo+lZadpIhQ51E1rXL5UIoFMLCwoI0SSAYRcd3Z2cHOzs7GB4ehtlsloL3tVoNFotFGE/cq8PDw4hEImg2m0ilUigWi1Kj68SJE3C5XLh79y729/eRy+XgdDqRTCal2D/nu1wuY29vDwMDA8K0I2BCucw6JIFAQBhcBIYqlQo8Hg/297uNbghe0U/5l3/5F/z4xz8GAGnsARyUCtEBbT7fUdo7DIBubGz0dJfTLBFjTU4NshrtMv6N/iMBPO2XAYcHn3UQUN8f/T9iBgTqqOe0bUowhPerr63Pib5fXpNMRdpAxlQn47Pz/9oPZmAtGAxKQCCRSEgK6icZjx2o0UZNf38/ZmdnEQ6HhepHai0jspxMAjtutxvBYBChUEgcNDqTOlLLiWaUju3ZOLFG44bRFR58LoJmW2jHU/9o+icdJ7ZfdDgc6HQ6kjbi8/lw/vx5TE1NCQr7pAefkY4Sc/pIXaODRGCCLQ61k0+jk7nBjUZDouI0LEjnJjpKBJpgj47qabCFa0WlxvnkvesoI+9HO/cEUjh0VFj/qx1JrqMG/Dg4RzS0tKLhPVFYU6gaK6A/7sHDrw0WOjlakOt9akS6WZjN4XAIkKiNh2azCZ/PJ50NiPhynkifpnGoa2Xwe1qtbr0GAI8wmFwuV08BaU1RJCuGwlILWrZT1ICEPq8cBBm5phaLBZOTkygWi7h//76slRbQvHc9V58ksvdpDio5Mri2t7ele4d+nedQU9RdLldPahHfr3/47JSdfX19PZFck8kkHWX4u1ZelOXaKNaD381rcQ3Hx8clPZKGDNNGs9ksisUi6vW6UPvZseG4ADJ/amijpdlsSr0HKu0zZ86gr68P7777rtS/6u/vx6lTpxCJRGA2dxlhwWBQziIAMZa0bCO4zPUzBiso0zqdDoaHhwH0njXKRs2moKPO9WKxTKYYhEIhLC8vIxwOw+/3Y3p6GuPj4+IMZzIZvPPOO7DZbJibm5OOFwTyjIDocTlvhw1txDHgYbQtdA2Ber0uzjNBE9LXw+EwkskkVldXJZWU8k8DcDTktB7TNWQAiHzlOlMf6O/jmSMotLu7iw8//FBo9joKTWOURiafiwavDmLx+5/k0HKbz8zINNnZugA5ASmy+GiPUr+FQiFx+hiIIsPb5/NJGgoAqXNDxhn1FcFyrqPVahXnrVqtIpvNIpFIiB7sdDpy9nW9IspgpsEw/TEcDksqWr1elyLBPp9PnjWRSKBeryMcDuPZZ5/Fc889h/HxcUnPNpm6wdCVlRXE43GcP39ego2ZTAZWq1UYc2traz0OzWGM9U9zUFYQEAkGgyK39Dqzg1e9Xkc8Hkc2m5XivgS3arUa3nrrLSSTSfE3eEbIfqGzbDKZZO05/H4/Ll26JAwd43ngmaUNTXnQ39/tLjQ5OYlqtYp79+4hn89jaGhIdPatW7fws5/9DCsrK8J44h4ZHx8XnUxbinLoSa2Pthk0mEmZSb1OMIoyZHJyUhiZv//975FKpaTQs9vtFjC22Wzi5s2bePHFF/GlL31JutGVy2VYrVa4XC5JgWI3JrZG1ixEBq2s1m5HzJGRETz//PPCciAwyrQVygC2fWfDCtqYBBvIiKLdQpCTPgflDsE/AqmtVgtXr17FrVu3HgEFKJc1WK7tvqMA27iW7FpmNptFbgAQ/xE4qPOpg4AaXNXBAe4L/l8zVziH1EfUSToIeBj4yICezWbDyMgIhoeHpeGQcfA7D/OPtK2r388ApWb+87M60KSD9Ic9M9BN06W9m0wm/1f1hY4ERdAOazgcxvnz5xGPx2Xzm81mqYIMHNDhOYk6517/jQaHptixtgwnUl9fGzese0IHRlPrNSrIz2rDB0DPxhwcHMTGxoaAC6wE7na7MTY2JsbUzMzMsQBquJn0hq9Wq8hkMhJ94euktGvUEoAYcQAeiZBzTciqoSOnDwvZKgRtKCiNIBmdTG0Y03DUbBoqO50jSedFv884KKwpjIn4azSXg3vH6XSKAaSdG10XhoWrtNPzOI3YwyiD+hr6mfU+5r3qzhVs5cvP8WxQEQUCAZhMpp7Wk6VSqacglp5P3odW1BrEIXjDM89oPg1Qggt63nk+aWhxjrkmjDBpJgANXn1vgUAAjUYD29vbUqjtMCT9sMjAcRmMEtNpYvSNMlGzYmhk0nCh00YHwsj24L8akNYOItCdExq4LNin5SrXknvJGF3nevL+uH7hcFjAIZ5zrcQBiEMFAB6PB4VCAaOjoz2RjeM2NOjBvcYCvARbCMLMzs7i/fffx8bGhsgjl8uFF198EZVKBdevX4fL5ZIiymStkInG7+I6MPoHHND0CWb/+te/RrFYxNbWFkZHR6VOCc8h75lyXEeUTaaDlAsAeO6557CxsQGXyyX14y5cuIBAICB76O7duyiVSohGo7Db7chms9jZ2cFTTz0Fv98v86UBreN07ji0LqGc0UxDBot8Pp9EbaljaMyTjeF0OhGLxZDJZIS1wrNBZqAuYgkc7CctF41zZjTeec5YtJpOHNlOzWa3UyVTe6mrKWO4Z3Z3dxGJRGCz2QQQ2NvbOzZBKNoF/f39mJqaQr1eRzAYlMLc8XgcZ8+exeDgILLZrNic+/v7wsCmHUO7kCB4Op2W9DACOpRTTHUaGhqSyC47p7FmEPUW158OBYN7lN38HNDLGiWDg8WHCcjSmc/n81K0lPJ7fX0dt27dwvLyMvr6+pDJZGCxdBsHTE1NYXJyEqurq9je3pZA1MrKChYWFjA/Py91T0ZHRyVdhHVu9Nk0BkmexOB6MB2FDLF6vS4p0PV6HblcDplMBisrK7hy5YrYIWazGWtra+jv78fc3JwAbQyI0d8gYAccpHRre3p4eFjOOutXUO5r3QccyJL+/n54PB7Za4uLiwiFQgK2j4yMSDOA9fV17O3twev1CnChQVkyZ41NAz7tYSwroGUR14EpzgMDA6hWqwKaNZtNvPfee0ilUlIEnPUSA4GA2DPxeBz37t3DjRs3EAwG5exXq1Vp+sGUMZfLJWxVFuumrKScGxwcxOrqKpaXl1GtViWgoP1J+iO5XO6RIC+ZWWQ6sabq4OAgvF4vLBYL/uqv/gp/+MMfJPWLzPVEItHDMonFYiiVStIpmd9NmW2U/2SUPe6zSP8wlUpJyo7P5xP5GIvFeuwPjo9im/B37k9tf+vX+TtlI+0OHVA32n3aR2QASbOQSN7QZ5bzxjk1gky8x1arJTWKiEHwXgn2GcszaN1LP5c+Mes7BgIBZDIZSfnj+Lhz+9g1LQ+l2WyW1IkXXngBP/nJT8ThYMEntkjUzjUnmk693pwcVKpkNHCCdY0DDUpoZExfh/erNzoPsV4cOj86XzGTyWBiYkL+xug183lJvzwOg5uLoEar1S2INjo62lN5en+/2woul8tJJyMqqMHBQYk47O/vS7cBrh0Fs+76pNNTSCXkXGvlxXXk2mvnhvmnAHpqm+g9ozc4n9VIp+Ogca1fp4PKPaEpb0zzYstOfaBpZBNQpIA5DJ39vx16z2rhpudAX0+zoSwWCyqVCm7fvo3t7W1cuHBBwJK9vT0UCgUUCgXs7e0JU8zpdCKVSgk4AkCotqyKrwUszzZwUF9BOzcaXTdS5zl3XFPSTCn0+EPBrc++jjKzPgD33P7+Ptxut3RKKZVKCAaDPdfXTrUGbfRaflpG6WH7mL+TgqrbQQK950jLKs4Hc7+1LOV3Gp1jfU3uN025JXjNNTIy1bgWOmqiv5/rRCdH08IJvgIQFh5ZRKzwz8iW/u7jPigr6HzpvcqU02KxiPX1dVSrVczMzEgdjbm5OfT392NsbAx+v1/YZUxPWVxclCKjTO/guTCZTNIie3d3F9euXcPt27exv7+Pq1evIp1OIxAIYGRkBCMjI/D7/T25+gRr0uk09vb2xCFl6uwPf/hDeDwejI2NIZvN4tSpUwiHw1I3IJlMipygDM3n82g2m/B4PCJ/jsv4uL2kwVvKXpfLBQA9UVU+p07hpKyivuB607mnTKMhqqPkxpQm3gOAHhnFe+B7yDh0OBxiMFLWAxCAjgwAnmWmtRLc4Z4dGRkBcNBpp9FoSGHJJz10PaaxsTEAXV3NaHU6nUatVoPb7RZnikCK1+tFrVbDgwcPxOmnfuIc8SzZ7XbpLEO2Lw13n8+HYDCIQqEgNZyYZlUul8Ue0IVjbTZbT4F8njvaTwwOsQg/Wa60wSjPWe+ov78fhUIB165dw9ramtgkZLI3m03EYjGcPHkS8/Pz8Hg82N7eRqFQkGAJv49ndH+/W3yZDg0DrUab+XENXoPDGEHX9hkdIDKfyJSgDiSDgfWA7t+/j9OnTwtIRpsgl8vh2WefFXYOh3byAAiYTdCGsrBUKqFSqSAQCMDtdvcE7HQKF3U2ZQKDWpzHZrOJH/3oR3j66acxPz+PsbExOJ1OzM7Oyn5msJOOKc8874ddiXTw7LCAJedVB6Ifx9C6njJM6/ZkMolsNot/+qd/wj/8wz+I49pudxslLC0tCePSbDYjEAggn8+jVqtJWlG9XkexWBRmEn0uBkF0KYLd3V3pGMU9ozvJkp3UaDSQyWREfrDJBZtV6HlLpVICwrCsA5+dBASgKxuWlpZQKpUwMTEhhW8BwOVySeq/3+8X1h+LgdtsNqTTaQlqa0YkbTGu31GMVquFfD6PjY0NNJtN6W5FebKysiJzzv3GtdZ+iPblLBYL/H6/ANiVSkWeQdulDC4cVkOSr2ufwGjDMmjFob/feCa078r15fNzTfkcZEzW63UJDrB2LsFbfaZ14Jrfy5TJ/v5+pFIpFAoFBIPBnhSyjxqPHajJZDL4zW9+A6vVimeeeQbj4+OYnp7G2NiYOFB0wowTaIziageC7+cPBYB22LWTroWknnDNoNDUJ3aaIs3V4/EgGAxKNXbdThWAvL62toaVlRXk83lh1LTbbSlmdJyMUQA9h4pRIv6dVb7z+bxQcDk//f0HbdY1w4GKhqkpGiAjIqlp+YzuE92m4gLQg2I7nU4x5gm0MWWOQAojgxQEZrO5J73lo6ILPPxkJdA41iwZRv0BiHHH1B3utYGBAXGgNTXwcStA3vNhwwjSGBF3rkWhUMD29jZ2d3cxMTEhc1mtVsWQ4zlirQJ2juCaJhIJjI2NSWRVfz/vj2tsFKJaYXOuNT1fC0wKY4I/pP2S3cM14XppRwZAT6tjr9crrDei40bhb5xLDi2Pjnp83H4h0GY2m2XumTLGs8H7JfCokX9GxmnQA70MLBp7QG9UhPfFtdEKle/l61xznk2toLQi5H7S6Tm8F6btUEZUKhW43W60221J+9Ipmf+vDIvFIkXHHQ5HTx2DTqeDM2fOSPvjcrmM/f19eDweTE1NYWxsTCjdLPJOoPHu3bvSVcLlcuHEiRNwu91wuVySYsH0VL/fLx1Nnn76aTgcDrz00kuwWq14++23JTIciUTQ6XSkDgbr5vj9fokEraysIBAIoNls4s6dO5ibm4Pf74fb7e4xJK1WK8bHx4VlU6/XhXlCMO5Jgm2f9NpGO4T2AlPB9N/p4NHIBg5qwIyOjoqRp6OLQK+TyiCJfs0os3Q0VZ9Rvof1MbQs1kGSdrtbkLxeryOfz4thyTNKWR0IBKRTCQ3Per0u9s2TBksZ6a1Wq3jjjTdw4cIFOBwO7O3tYXR0FDdv3kS1WsXk5CROnjyJtbU1ec3v9+ODDz6Q9s10LjudjnQG6nQ6PUw4DnZwIqjC/UwZS53FFEI6hOwAA3TXiCxFgiWUfZQNvC5ZI0wfJPhO+210dBTBYFDOvGaB0MZptVq4f/++yIuRkRGcOHECQ0NDEojr6+tDJBKB1+sVFqoOCvF8HwWbyggqGAEHDgZGCQgT4HS73fB4PMjlcsI8XF9fR7vdxuXLlxEKhYShybVgsVbqHSP7hTJN2w1cK55lgga6+6nxmRhx1wAL2Y50htfW1tBoNDA3Nyf2ud/vF7uHwFSz2W1H7fF4hNHucDh6nFWj3aJBZv7/cQ/NvqdtQFuAwdvJyUmpb8aULZPJhM3NTVkrss74nHxut9sNp9OJYrGImZkZLCwsSAphKBSC2+2W88K6NJ1OB0NDQyIbBwcHBSin3cIzyPoh1F8EVnQ3XLJN2FmMPojFYhG2Ngu+u91uXLlyBffu3ROmDwsOE+gjEMUi/PS7KGfffvttRKNRfPazn+0JiHO+9Zo+rkHANplM9qRSExwNhUKi26jrqL+0LU57lHsxl8uJzNIBBL5OP5OZFloeaN9e+xIERPl+3hc/o8FQHdjQdivQaw/zmXhPtKl5feoJ+kj0nTUTnWean+F+Y3mXra0t0S+f5Cw+dmlLSqXVasXCwgKsVitCoRDOnj2Lra0tEfL9/f3w+XzI5/MSQafhQSFoLAzLjaLrWWikmmg60Btx4mTwdS34uTA8bDrKTwObhhnTX4rFIqLRqLBEqtUqCoUC2u22FMjUkbInPfQG1DmVpL3TMU6lUrh165YworgWPBisacO0pXq9Lptdt0mjo7i7uyupKATCNPOEaWs8SI1GQwQDqdikBpOyDBzQzgA8guRqUE9T3nTEnntB05V5cLnXGD1idzICAsDBgR8YGEAqleppHU9hrqM/j2sNDzNk9OvG62lWTT6fx/b2NiwWC+bm5nrmnGvM+Wo2m6hWq6KgOG+7u7uIxWI9wAuNFwpI3qcWkFqwGudHR+e0gOf98BwODQ3JdzQaDTgcDnHiqVAJvmnGFM98JpNBPp9/hCFjNAaNQM1xGDQGCYIBkLnhoJIkk5FACVP3WKzW6CAC6Nn/h0VP+b2MKGrghZ8lA1KfEW1Y8BmonEl5NgK7+l4IRNEocjgcSKVSAA5vU39cxmFGcTAYxIMHD8Tp3d3dRaVSkSJ4ZIPREKG+4RxQqfMMDA4O4jOf+Qyq1SoePnwo5zSTyeDatWsol8sIh8M4d+4cvvOd78DhcODGjRsYHx/H+fPnkclkcOvWLVy6dAlf+9rX8Nvf/hZ37tzB4gCG2VkAACAASURBVOIiTCaTACo0pgmaE5AhW+bkyZOYmZlBKBQSx4PgqtPplPTfcrmMTCYDp9Mpjoc2Oo2RruM2jPua9HSCGty/DELxzLTb7Z4i7Y1GA5OTk8jlciiXy2Lccz/v7e1J2oYOflAnad3E4qW8Ho1es9ksephOJF/XZ7nRaEhh53K5DJPJBIfD0UPXHh4eFjlLRorJZEIgEOhZvyc1Tpw4Ab/fj2KxiFKphFgsBpfLBb/fD6/XKwWcw+GwsCmZRnL37l1ks1kEg0FhFFE2URfRvqTjxHXUTiYp8ENDQ9jf35e9DxwUuGU7VjI8TaZubQW73S4RZOCApUNmKNlnLExKcIiAg8PhQL1eh9vtxmuvvYa1tTWkUikkEgnYbDaUSiVJpWEkl0VYga4MHx8fFzaB0+nEqVOnJIoOHNRNIrj8STuVHPUolUrY2NgQvUQW4jvvvINYLIZUKoVwOCx193R6Xy6Xw9LSEqLRqJyFcDjcA6bogJG2UQjQECSjj6LTWrUvoYtya8dUB0AikQgikQgCgYDY2LxXAlF9fd0uZPV6XfyPvb09lMtlaeXM738SvocGlLXtxzmkvf31r39dmJcMkt68eRP5fF7qQOlUzWq1ilwuB7fbDavViqWlJTz//PMol8u4fv26pCYx6wGA6MNKpSI2D4M/umwDAaRQKIRarSbzTPCU602ZyGCy0+mUdEbul3w+L8WR6bPcvXtX2Dk2mw2xWEz8Stqq+/vdeqFMm+P3ORwOXLx4UeZX+7lGef44B+UPAWOL5aBgN7tc6TXnvzw7tFPoY+rAugZHjGdN6xM+n9HG1LpVl9YwAi3cg9x/9Dm0TcbPs6yAfg59TqnfWSeI16MtTL9J62IAwkjkPiYLiTqAJUY+yXjsQI3F0q2yf+7cOSwuLmJpaQlDQ0OYmpqSTUqF4XA4kMlkRPmPjo5KmzIW/2TrOhoJmrXBBeeE6v9rAasNQAoQ7Ujzu0gh15W8aZwStKCRzM/39/fjq1/9KsLhMGKxmFyXEYfjoNAA9CgHUttpJDBCS0MDgAisTCYjkRaCMTxARI55AIHeA0AKNlFHjVJy6MOt2ynywLBFtlZ4ZDnRmOG6aqf7o+hkdPzZnUHvB42atlotEcJ09jVyzPshkMeWkEe13vrZDnOyaXxrNg3/vru7i52dHWQyGYnEGEEnAiEa6OBaM22Q0QPOnXYcKXC1Y8FzQLBOAzH6h/NPgaoL07bb3ZSBSCSCcrksyt1ut0t7aip23oeO+HJ9uV8Oy/XlmuloGgXwp23sfNT1eF98Vj43lYRmp9DI4zMwQqW7l9E5oNGtFayOMlDhVatVqTOkZYlxT2qQlCAcjR1dz0bTYrVS50+lUkEqlZKIebPZRDAYxPr6uqQXGOfmOA7OC1PQCGjyLAGQzgUEOijnWF+pVCqhXq8LuEzwZmZmBn/7t38r+c6tVreI6LVr13Djxg2YTCaEw2G0Wi3cu3cPg4ODuHv3Lu7fv4/+/n5kMhm8+eab+OY3v4l2u43R0VFEIhG4XC6Uy2U0Gg0MDw+Ls+hyuWC321EsFhGJRAB0597pdGJoaEichkKhgNXVVYyMjIjzk8vlZN00wEhZBRwNE/FxDH0muW9pwJKtxvkpFos9rLB2u0uRHhkZkU6WY2NjyGQyYvxR3wEHcrCvr09YWFqu66KSZLXq86/PNh0XzZKjfOjv75d6Qy6XCz6fT854u90WZtbExIR0aSHgb7Va4fP55H6PIqr7ScfY2BgCgQBWVlYkwBAIBFCr1aQhxZ07dzA+Po75+XmkUinEYjH84Q9/QLFYlK5neh8CEKp+rVaTgr3lclmcMOpjBglqtRpcLpek9evaUKVSCT6fTxjIBClZ46bdbksXKrJ2mVbA9FACNG63u6fjF9DdnysrK3jnnXfw2c9+Ft/85jfxox/9SBh89XodhUJBAJlMJtPTkSqbzWJmZgZ2ux3b29uYmpqS556enobb7UYqlRJH+ajW+jDbhs+nbSuyHhik4ev1eh0zMzP493//d2krHAqFBHxkQ4zt7W0sLS1JS2uXyyVOImvN0HYiGEP7gvan9iW4N5gmYyy0TZY20z34THyPxdKtlxGNRnvSl7mHaMORWZLP5zE7Oyt2Klu+ezyeniCZPpdG+/Eo5Kzxejp7QdujrG9F0I8BQF0XTddlIdCfy+Xg8/mkW9YXv/hFlMtl5PN5sTd0PSez2SzBdaadMhBJdiqDIC+88IIEOJxOJ8rlspRZ0LKVMpiFqHlWk8mkAE2spRcOh4XVWCgUsLGxgVAo1AP2cM2azaYU8SaoxbpvS0tLh6aa6/PxOAd9RG3r65QjDRTRN9BMFqNNaLw/+ou0XemDaIBHgyR6vxqfndfTek3bsxrc0uCnvn8yRXkf+oc+BW1X4MA/5PnnPjXO4d7eHjY3N1EqlSQt3eFwwOl0St1M2nt/ajx2oMbn8+HBgwdwu90IhULY2NjAgwcPJCeXeX00tFmJnnmCdBqHh4fFeafzpQ1/MhoOo09z4TQ4wI0B9KZV6Xw4pkboCJlGxunYED1jWzjSw6enp3tSqo5LpJdCi04YnS9GAjqdA4qtFmA0CCmgGD3kPNKIpPOlqdcAJIJPoIeHlwYRlREAmVvm8TFiwPdxUJBzH2lBysP0cWvAdRwcHBRGD4vZDgwMSAt54AAkIPVZCy1+N5kBjIzzGo9bERrRYB2x1cAk0JuSQid3Z2cH+Xwec3NzmJ6eFmGjhQ73NpWPyWQSaqjf70c8Hu8pIsYzogtFGwEzXU/FarX21Dbic2klQFoz9xS7M9TrdUkxY7SZz+fz+WCxWKReVKVSQSQSkdeZ/6+LlxsVnf5dM34+7aHXWA8aNFqpEdXXTgb3IM82lR4Nfs1S0o6xXjOuhf6dDqJmL+m50vtTg2CUj7wnym29vwjS0IBixzWmBnAtCoWCOML/rwzOS71el24vfX19CAaDkj7k9XrhcDhkbxsBM4LIeh5JkWYRTdaQCQQCKJVKWFpaQrlcxo9//GM5S6+++iree+89ZDIZ+P1+MWx/85vfYGxsDJcuXcL4+LjIWODA6RgaGhKmKAFZAAKa7u3tIZlMYnl5GYuLi1L/hlEmAOLw8+zSoNLRsOMI1Oh7oq5kiggN/aGhIUQiEezs7Eg0EujKYqa/pFIpOJ1OoXZTPmkmG6OrPK+6NoYGPd1ut8hEnUpOmdlut6U1OOdWG85M18nn8ygUChgeHpa20h6PByMjIwLAcT+0Wi1pX2tMc3tS6+b1ejExMYGbN2+i3W5ja2tLCoc2m02cPn0a//Vf/4VisYhQKITd3V188MEH2NjYEGeI9edMJpPUvKBOJNjC88j/k2nLz+XzeQHl6Dx3Oh2pY3Lp0iV88MEHyOfz8Hg8UlT/pZdewurqKtbX1wVk09T/gYEB1Go1+Z26vb+/H9lsFq1WCzabDblcDj/96U+xu7uL4eFhTExMYGlpSWQtHfkTJ06gr69PWlUPDAxgbGwMly9fls42zz33nOyLX/3qVxJIHRsbk9T/o1hv7Zzx/7TjqWu0LUuHl44f7XHKOaabEVCLxWLY2tpCKBRCKBSSbogE33iuqfPIjtLglAZAOp0uM+fmzZtS81F3FdL6j8+hWcW0l2m3hEIhZLNZ5PN5OdvsZsvPsDYm9zJZfZQzxiCYPp/U0xq4fZzDyLLWa0VghsFuAD0MhEKhIIEl3bHXZDJJAJndncbGxpBIJJDP53Hq1Cm8++67UieI7EDW37JYLBLoJyODNi5lrdVqxcWLF7GwsCD+AGUxAPFDCNzp4D4AAWIikQhGR0exubmJcDgMr9eLnZ0dAd9GR0el/TZtK34nWai6GQOB1L29PXmNOoD3Q1/ucQ7agQwE6nWkjU7dpf0rHawHejv2ajuR/+fzMKCrr8PzrdkwfJ1DP7fRltV+iA7Oal2owUTt4/N7NDhjBGm0n6mfXetZgkA7Ozs4ffq0sBKpr6kzPsl47BZvKBTClStXetpQxuNxmEwm5HI57O3tIRqNChBjNpuFYr20tCTt28ho0AJZo3RcYA49+dxo/NEsHL0ZgAPWB6MqnU5HWBz67xpBIzq3tbWFTCaDzc1NKR6sc9OOy9BzowENDTyZzWYxWCwWixQWJNqnlQ43K4UZjVcyjWi8kuJPo1PXQaGDr9eESo70ejqVJpMJ8Xgc4+PjokD5WS00yLTRh9nocNPp4YGk4jKbzRL1J+hAEEYzPIDelnSNRgNDQ0M9NW2OwojR36mVrTHqxKF/1+mGiUTiEbCM6XtU9iaTSVKfmCtvsViEFUcaNvPgec50O18KcZ4pKmDNwuCz6HPLe+MPQYV0Oo3BwUGpv8FaHVRoXC+2QOTaWq1WYWLo9Dlt9On5MkaejsMwmUw9tGZGW/U5BA5om/pzfAYa/0yX0LIROGDP6H/5nQRp6AAmEglhahiNaD2nPGv8fsoKzbijsqQjSkCffyN4SsWuO9Tp+z+ug/MSjUaxt7eH9fV1cRAIENO5piymYa71DQcNTDqDmuUIdNeZLSD9fj9KpRI2NzeRz+fxi1/8QtKtKK+YOnXmzBlpra0BIgLYuj4YUyK1DN3d3cXq6io2NjZw7tw5qa3DFCHqTALslCnU8dyfx3FoOcA9brPZsLm5iXg8jomJCTSbTUQiEUSjUSwuLgJAD2CysrIi3Uzu378v7Bp+P38IhHKPcA1oHNIOocFeLBYldYe6kgAeGXRG3cDzzFoDpVIJW1tbUrCaa0HWEB3WdruN5eVlzMzMiA7VBuqTGG63G6dPn8ZPf/pT7O3t4eHDh1Jjr7+/H9PT07h69Sp++9vf4utf/zpqtRqmp6exuroq6T8nT57EwMAAVldXpeYcWbxMSeA+pe1RKBSkYDAdZQYZGcxgemOpVML58+dx/vx5fP/734fJZMLMzAzOnj2LaDSKt99+G8lkUtpuk01FWcxUJZ/PJ4EODSJxzVqtFtbW1qSAMOUp739rawtjY2M4ceIE9ve7hVi9Xi9efvlljIyM4J133pF1pT1w7do1STt96qmnesCAJzWoo8n6Bg7svHK5jNnZWbz11ltIp9NiM7JV+8jICGw2G6LRqIB1WpdokIGOPL9/YGBAAkm1Wg25XA7379+H3W7HysoKbDYbTp8+LWdOpzzxzBmDbXRSbTab2CksHbG+vi4djMhq3dnZwbe//W1hiVDus+HGk5ShWh9rG4/gFG3+0dFR5PP5nu6rDNYTfGq1ug1PaJezkGuxWES73a0BynpMLHxNX0UHaukUAxBGVafTERZUo9HAa6+9hqeffhpXrlzBe++9h2QyKWnHDocDhUJBGK8MLptMJkl/Ag5qlm5vb0ur9larhfHxcZkPMqq07cuzxnvR4CN9Kq1nD7NLj+IsUgbqupCdTkeYXdpGNNrMeo9rv0UHQAmI6mEEPTSYyaGfX9s81I+HYQL6O4zsUtpn1KkakNE+M3CwjzUopJ9BA8tAl8XvdDrFhqI9x2A/CxPr+/0o3/GxAzWMNHU63Wr4U1NT2NzcFAWaTCYxMjKCYDAojBhOUDgcFoq0bn1MY4ETppkLQG8EV+e/GRdYD72pKCwIKFBQtlotae1ns9kQiUTEaFpbW8Pa2hr29/cRiUSwtraGbDaLoaEhzMzMPAIsPcmhDw+RbdZ60A4R2SxkPtC54vuIdNO5Z4vVbDYrykRHWnXaGXAArunDoh1FUsPYLYvKmOAXjQ79PPq7DtvkhykugoBaeTKNSiP6nAvNxNK1jEj1D4VCPdc+inWnwOG+NjJs+B59DxQctVoNiUSiJyeeUQKTySQAFVMUGEnVeb6sP+RyuXrqnXB/0KinsNQR4v39ffh8PmEd6Ur2vE/N9OC+YzFjPpvf7+8puMgiq+yEBECUC/eT3W7Hs88+K9R4vk8rPiNApKm62mH+NMZHKWKr1SodODTqT5YKwUQaKEYQSstZKiYNymhmjgZeyuUy3n//fbjdbni9XmxubmJhYQGvvvqqVPHn92tFqUEbUmW5ljoqydd1xIPfQRCCoJzZ3M3l1zTWowBFH8cwRocInESjUQEQdQomjTOuH41WzWYkMMcIu45gAhAKr8PhwNmzZ0V2sWsW2TCUq36/H/fu3YPNZsPMzAwAyDW10691Ga+p6zGQSfDMM8/gmWeeEQOT4DzPO9tRsnArwSpd3+s4gjXaEAUg+tJkMiGTyeDEiRMCRs3MzIhTqNMWgK5B2Ww2JW9dF1XU1yIrkPYRdS6LTVMudjod0eWavUSHRBee1vvEbrdLsUgCdozWM2DDQt4MujSbTZTLZWxvb+OVV14RsO5Jr9fg4CDOnDkDv9+PWCyGeDyOVqslaRXlchlPP/003njjDQEk+vv78Z3vfAe///3vYTJ1U9udTid2d3exuLgoupGFlkulEtrtNvx+v5xb6gRGx6m/yIRgamGhUIDT6cTp06cRCARw9epVAMBrr72Gc+fOSWCSNaW4RnQodQSXIBK7M1osFmEEsNg+u8kAkD3qcrlgtXbbfG9tbeGpp55CJBKBw+HA/v4+ZmZm8ODBA9y+fRuvvPKKRHoTiQTK5TLa7Tamp6fx/PPPi/N8HOSurgtDcNJmsyGTyeCVV17BD3/4w57aQYFAQGrvEJRh4IrBNh0E63Q6PamAwIHvUavV8Prrr+PChQv44he/iO3tbVy9elXWmoAXZTJZ6ka9xy6G1Gler1e6h42NjUl9k3Q6jRs3buC1117DyMiIOM0EWmOxmICL+jqf5mBATusm/by0xwBIC2uembm5OSlqz9RR3a2S7KLd3V3kcjkpzstgIYPKlJcEl7X/olnVrGn1ne98B4ODgwKmWq1W/PSnP0WtVpPC3fRBNDhmMpkkG6PZ7HYCrlQqArYSqNO6m8A25QNlNeUH00r5HmZtlEolsXu0r3uUPiYDtAsLC/jiF78I4KAbLteaf6PfBByUH9Hv0f7JxwEbWq7wfGpWi9Fe189vMplEntGON15f+5qa3cPv1MHLdrvdEyDTbCEjtmBkCulBEJaBKX4vg196fJxMPRIOOTcalQQVDgsUZjIZJJNJjI2NyeRZLN3aNna7XTYsHVNObLt90PqbUdbDFs/4d6C3OBGHEd0rlUqCTJOqvru7i9u3b6PZbOKVV17B6OgozOZudygyRlhAcX9/HxMTEz2RsuOg0IDeFt10ZLlOdKIIrG1sbPTkY7IdLN+vnV3dKp1/Y4s5zYjhQWauu45aAAcoOB167hleV9eAYYToMPT0Tw0KXE3h1+wBrWQYPdHGqAYK2IXBGOU/CsGpgQMN0hifXZ8Fvp+5++12WwrrEURhhycAAkLW63Uprkan0Gw2o1QqoVqt9tAgeV+68B7XjnNttVoRCASkCxOL+mqAhBR+4IBBwO4M7J7BKCIZb+xopPexrsTPc7mwsNCTrsZ9ZwR8uQf4tydhkOr1038j+4G58jxbxn+1DOTcc5/z2QhUUqZqpcY5oBOQTCaxubmJV199VToTra6uYmtrC16vVz6jQTeteDVtXDu6JtMBE4rX557k3tEpHwQcvF7vI4paj+MAjOvB9czn87Db7RgfH+9hazI1lO9jqkQ2m8XOzg6CwaCAO3T4jbRhsiRbrRZisRiGhobwmc98BqVSSUAyp9MJr9crkRwaNDMzM3C5XCJTScOmPOa5pgxhC1xemyCb3W4XZ4Z7SrM6uP+i0ai8j+eVxruWW8dpGM8j5Vuj0cD6+jrOnz/fE+w5deoUFhYW5Kxqe4hOH/c3DT+gt7A6HXXaI6zRNzMzg0qlgq2tLfh8PoyPj+PGjRs90UCmLxP8o/PD76GcrlarUmONn2WqB9eW9VAI+Fsslp7AxJMORvX19UlB60QiIWcI6NYEKRQKGBwcRLVaxdtvv42vfvWryGQyqFarSKfTSKfTaLfbwjIzsogIgHOtqtUqvF4vQqGQADi7u7tIJpPY2dlBNBpFMBjs0YE2m01YbqdOncLo6CguXLgAAD1MSa6FTmtkK1sa+8ViUdjGxWJR5CadRpPJJOfXZrNJGh7PdSwWQ6FQwOc//3lhMCcSCfzyl79Es9nE1NQUtra20NfXh+XlZQFuJycnMTs726NPjiqAofWFDihw0F6z2WywWq1IJpMYHx+Xfdzf34/Tp08jFAohkUj0MIVpszudTvh8PtRqNVSrVWFX0c7UfgIDepSlZNPduXNHgKvl5WXcuXMHMzMzOHnypAR8gYOAA/UbgRm+rtNMz549K046230TlPH5fPiLv/gLYXrQB2L3GaNjqQMfPK/a4XzcctboZOugkS4Twffy9b29PUQiEczMzEjHLtam4z7m+yi/KpUK7Ha7pA8zsMf13d3dlb1KsI17aH+/W0j4zJkzOHnyJN58800kk0m0220Eg0HMz8/jww8/hNlsljpXBAX1/GkHnCCODkq1Wq2erroE5WkrMc2K5zgcDgtzqFwuw+v1ij3O7zWy9o8ikGixdJt3hMNhbGxsoFgsis9D+cih54Of5R7QdhqH0Rak/af/xvdwbvU+1T4GAAkG0QaiLUJAjmeZ79eBSNrLxgAhv1fjDtrPMd6/8WwZgxcMcrhcLrknBmo+6do9dqDGKFDZzz4YDMLhcGBiYgLZbBbr6+sIhf4/6r70uc3zuv4AIAliXwmA4L6ToiRq8SavktwktuM0dZymTdLMtJ3pZLrNdDr90D+inem3zrRppsuH7m0mHkeuszhWYsdLtFELRS3cCRL7SgAESeD3gTmXF6+pxPmNJDPPjEYSF+DFs9zn3nPPPTcknZN4GVIEtFAoyERSp4LBBQMJTiCw1yZMO3ucZJ012tzcxNramrRk1BurXq9jdXUVi4uLIrBXKpWwsrKCtbU1WK1WnD59Wtgd1WoVwWAQm5ub8Pl8OHz4sFy2eoMeBOeTz8B5pvOts7IWy67GAcEadrLSwQSwp1tA54UtHMnW0OKHNJYaTdZUfc6T1WqF1+tFd3e3OBZEyGloGagQHOPFpAN+/Xk5+PV70du01pF+dq2fwAPO4JFzx4wT99GDWmvud+Pno6OmwRPj56PjziBY04RZ4sSgie+VyWREF0NnvIlwa6CP768DSG3gOjs75TU6OjpE8I3ADH+P700DzUCHXTgSiYQw7XgZk/6sLz+Cv3SU2RKV68q11JeG8QLQbJRPevBsUVOJzre+IPTeoINDO6k/CwMI7ls6ILyAeM54ThOJBOr1OlZWVjA6OioCpAsLCxgdHW1yDrlf+FzMiGmHm/XWvFg1e0k/587OjnQE1Bnl0dHRpizLQbCtH2fooK+lpUVa4urgqlarIZvNYm1tDefPn8fOzo5ks6jVw89OsJM10NTNYDvlgYGBpuw635vnhXpAExMTqFQq+OEPf4hKpSIaFgQDuWd4Lgl48uwRkAcg7Ds6zvqcWa1W+P1+HD58WLrJaNt1ULL0+w0jOM57JBwOY3R0VBJH7Bpit9sRiUSwvr7eJDxqsViEKcW51OeHdwuz+tSq4fzs7OzA7XZL163+/n5sbGzA6XTKueXvGang/H1tK5j116wmAFIuyuxua2ur0LQbjYaI0X/SbBpgdw95PB50d3fDZDLhs5/9LHp6erC1tYVkMolCoYCtrS0cP34cly9fxqlTp+ByufDhhx9idXUV29vbAhwyiUXQkgwAluzlcjkRpmWwWC6XkcvlkMvlxNfxer0yVy6XSxJdfr8fXV1dwrhgEDY/P9/EgmCCgX8ASODBYFSXi7KjCHX3gD1mHIWE+WwETwOBgLByvvWtb+Hy5cv4vd/7Pfm829vbuHTpkoAchw8fFrHbh21z9b1sBHH8fj/W1tZQrVaFFRUMBpFOp/HFL34R//3f/41z587B4XDg8OHDiEQicDqdAlxnMhlhPbW0tMDr9Uq3LC3KTbZOW1ubCNvW63XMz8+js7MT8/Pzcp+m02l0dXWhpaWlqcuQkTVaLBbFB6NfymYmBNNpq2OxGJ588kk4nU7E43H09fXJXBBcNbId9hsP0p8xJhB10o53F1mEZD4x0Wu1WkXHaWlpCaVSSUoxeZaYsGMcsL29jWw223SvEkyl8Dd9O5YsUZQ7FAqhv78fq6ursNvtuHPnDlwuF65du4be3l7kcjkRV+deYzkUk0+0DyxHzGazAuTR7yagyZiF9yZZk2wEoLXK+DtkteiOs7pKQSfH7vfwer2IRqNwu93IZrNSds4zQB/O6EsbQRZjeT6/p+8l7hUj0KFjeCMbhv8nMGO324UdFwwGm/SD9ku06HvwXgn2/eIB42fQd6xeC742Y8Z8Po+uri4Ae8yeXwZgeyCMGo1iWSwWccy6uroQjUaRzWZRLBaxvr4u4oVEhU2m3dq/fD6PtbU12Gw2eL1euZz0ohGhzuVyCAaD0hFDo7k6w8wMvNbH4SgUCiKadvPmTfh8PkxOTgIARkdHsb29LVRWGh0e2mg0ivHxcQEw9tu4n+TQn9NqtcLtdktrPxoiUjHdbrdQLm/fvi11kxoAMJn2+tXz8s5ms3IZaU0D/p5R6JdrxOew2+0YHx8XkUK+DrAX5JABwmfW2Xa9L7QTwcNGA0dkmFkLrQtBRJZGQ68xa0k5H5oCuR8t8UE4Mvoi5OcyGhmj0SB7yOVyCROFmXKybDQarlF7XXbY2tr6kaCMn5NzrwFR/n5ra6usaa1WE2NPwViK+On6TxpRs3lPBJegEC9tDdBo5lWjsStMFwqF5ILMZDKw2+2SNeVcagaKBnA0cHMQgkd+Xo/HIzXadFZo5+isG+mkzDBqII8/w4wOAVz+DJ3DUqmEgYEBEchLpVKYm5sTZzaTyYgtYVCnhTd1WRw/BzPV3JtaA4v7mefV7XajXq9jaWlJWIzMNu/HhjqIg59H02M5DwBEZ4bC7LlcDjdu3EA8Hscrr7wiXSGYDc9mswiFQhKcU3OGgekTTzwhzNTW1lZxAl0ulzhMrP/v6uoSNs3S0hISiUQTC8pisYi9TcBI7gAAIABJREFUoE1n8LG+vi7MPLZvrlQqSKVSTd3FuOcymQwmJycxMDDQRB3mnj3Iw8gSIwMjHA7j8uXLOHz4sKwRAybeBwSgWdIbi8XEsdd3j87Ecm+w8wnLx2w2G5aXlyVj73a7MT8/DwBIpVKS8dT2UzuUtNPaUeTXCTYRXIhGo+Kck92jAXa+7ift35jNZjidTvT398NsNuONN95AOp3G17/+dVy/fl1adY+MjGB6ehrf//738YUvfAGDg4O4dOmSBNgUH81ms1JO1NHRgXg8Lhow9I8InmxsbGBjY0O6Bvn9foTDYWxtbSGfz6NarQpQl8lkJDtP0LXRaGB+fh6xWEwYp/RtaPMByF2pWTfUAGTJHUFffo58Pi+MAGBPBDMajeLkyZNyt05PT+PDDz+Ey+XC8ePHMTc3h/X1dZTLZSwsLMh+6O3tbTrPn+S6c8+2t7eju7tbADfaEfoMw8PDcg8NDQ1J4xJd2ubxeJDNZsUfpE/J+0zbTCYNWWqoO2plMhlEIhFMTExImSlb2GtQjc9PZs3q6iqWlpbQ1dUlthGAgOSpVArLy8solUr4zGc+g/X1dWEW04fJ5/MCxH3Sg/4o7ZgGpxqN3VIyiq8TyF5cXMT//u//CpOGkgqME9kRKhQKiRgrPz/XxWq1SknpysqKlAOWy2WUSiUpdzOZTOjo6EBvby9aWlqQyWRgtVqxuLgordyBXWaV1WqVeJKaYj6fD11dXZienpaqEACynvybfgn9Wg5+nfpVTKDWajV8+9vfxmc/+1m5E202m3Rz5L1iBEmNCer7MUwmE+x2O3w+H6rVKtbW1iRGo6/H5LlOZGv/Wccf+72+BmFoSwhS8d7je+nYjJ/VWGpFYJpAtMViESBdnzs+nwa8dBxlBF70ZwLQ9LwEovS/97OPRnJAoVBAo9GQ7oAfZzxQRo3FstvikGUIPT09cukDQDweRygUkgwdsGs8M5kM5ubmcOXKFXR1dSESiUiGhxNJFgAzvDTOLIlhBsuInLGshotjsViQy+VE9Linpwdnz55FPB5HPB4X4WObzSZghNPplA4d7e3t6OvrE5AG+Cgd7KANZtIYcMViMZhMJlElZ5A0NDQEANKRi44cLx+i4US5dX0inXWCAvpQaEeSTJr+/n5p+UrKIIXAqONAB0mzp8h8AfayLBw8zJotoLPAWhuDJTQ8nDxcNBLt7e3o6uqC3W6XgwbsGnRjm29eCMAvh5r+vGF0jIwXIH9GB+QacacR4/kwm81NmiGkS+vnJahGDRu+LwAJRLRx0+AnAxEq/TN7SBq3zgAzMOT+4euy/peZKl1ewD3EQIYAA3WDjICTscSEX9dBo/ECfJiByC/KgplMJunoowV2dfkLAUgjUMzzwrUho4b1+7wsuP6k3JfLZdjtdnR2dqK/vx+bm5sIBAIYGBhAJBIRh4kZaK4nAGHhRCIR9PX1NV3OtJW0DXTqdG06929raysCgYC0t6Z94lp90oHifoPzz2ejDWO2nM4HM+ZkQJB5WKvVMD4+DrfbLWeAiYvZ2VmcOXNGWJ0snyKI0mg0RLsJ2K3Fp0YMAVGyRXkearUajh49imvXrsHj8QiFm50oEomEAPJ8zQ8//BDd3d2w2WxNDNNGo4HFxUVsbW3B6/WKnQqHw6IBQLv0oIHt+zWMz9ZoNET76l//9V+RSqXg8XiQTqelfIY6fdSB6e3tlUQDbRvw0ewa54ZaadVqFcViUXwolmrQb6HdZlKBDj7Q7PjyLiObh+vLM08n0+fz4cSJE7Db7VLmWqlU0NLSgtXV1abzxkTZJwm0seTh7Nmz+Pd//3fMz8/j4sWLmJubQyQSEQo/771vfetbePLJJ9Hb24tjx46hXt/V5ikUCnKXuFwudHZ2ShDFM8X54t3ncDiQyWRgNpvR398vQV4mkxEWK+/NhYUFdHd3Y2NjQxIP9Xods7Oz0sUrn883lfaSvaHF4wmiswOetjEE13nvUtPGbN7V9hocHMQzzzwjTBKbzYbz58/jzp07+P3f/30RsL1z5w76+/sFYO/u7sbY2FhT95sHaXeNr60DKe1b22w2DAwM4LXXXmsqFdnY2IDH40GtVsPU1BTm5+cxMDAAn88ndjCbzYqgbXd3t5TaAHvdWrTPyvOSyWTkDHd0dAjr/sknn5RgsVarSXZf+5hMKurPEQ6HhRXFsivqkpRKJczPz+PGjRt45ZVX4PP5sLKygvHxcRF4JkPvFwXrxsTxgzizGjihnedacS54dphY2tnZQSKRkHbpyWRSwEHaUe7LRCIBq9WKcDgsSXkG8NTvWlpakiA/l8tJmanZbJYSfwDiV2pghQm+arUqIA3XzGLZbWjBDm3JZBKpVAqJRAJTU1Oyr/Rdpn0ynSDmYBzLErxsNotkMildAqlNxPvA6F/rxM/9HPSdqXs2Pz+PcrksJZc2mw3FYrFp7nT1gRGkok9qnBv97EwKkblJ8JnANH1EXQXC19M6eIwTNzY2xMbR72GMR1/JqMuo2YLcs8ZYXttb+q8EYvnzem0YkxKvqNVqSCQSUr1Cm/OLxn0FajSaxE1lt9sRjUZls929e1cOEg8YJymbzWJ5eRnXrl1DIpHAzMwM7t69i8OHD+P06dNIp9NihNmKzel0St1co9FoMhDcAECzorTOGpbLZczOzsJi2RWyjUajgn4zWxMKhcRIMsigKj8zJBw6QOb4uKjZwxi84PjMOrvOtaNRcblcGBwcRGtrK5LJJFZXV5FOp6WFNfUOiA7qsgeyNRhI6M1vMpkENQ6HwxgYGEAwGBTHTwfY+rn13zqrzstBs3WMn40BvtfrFdqxPmB8dlL7eMFyvZm94b4ulUpN4rya1aODtPuZ5dDsD2MwbpwjZsT5GdniWgfzul6YoCY7fui11IZXD10aQUOtjbMW5+aaUfeHHTNyuRw2NzclK09xNf25uA9oSBmAaAeOwYmxkxippdQ20Z/JaBN0Bppn4SBl+7u6uqT1MUFjCl4TDGPgpR0HoHnvkOGwvLyMSCSC8fFxscf6zJrNZiSTSWQyGTzzzDNib9li2GazibNIxzafzyOVSmFhYUGEOCngqS/VRmO3w43H42k6g+VyWQAMtoIlWMqyEn5ObVMO4mAQq7MnfF6KygJ7gBWwmxkqFos4fvy4ULrz+bzQ2ylIq88wg3ev19s0Lz6fTwA3gm5MnjBjrAFRlk+ZTCa5Rz/44AOYTCZhoVLUNp/Po7W1Vd6b4vF0ShYWFjA9PY2nn34ajz/+ODo6Oj4CeGgbfpCH3l+0Ca2trRgdHYXX68Xq6io8Hk8To9fhcKC7uxuRSAT1eh02mw3xeLyJZUobxHlh4MK9brFYUCwWBVzhvUaGIFnIBFRMJpPcScCeXadTStvo8/nQ2dmJRCIh4BqdbTrIwG7yK5lMwmazIZ/PIxaLSQn7QWBWAHu2e2JiAmfPnsU//dM/IR6P47333sOJEyfQ398PYDfZNDAwgDt37uDatWuYnJyE1+vF+Pi42HiCMQRMyADlvUnAhGANk4dtbW3o7e1FNptFOp0WcIblSj6fT0rkuRc0yFWv1wXA2d7eRjKZRCgUEk0DJkoYMLIkg/ci70Pez41GQxJSlUoFhUIBMzMzUmL1+c9/HrVaDcvLy7h06RJ6e3vxzDPPCGPRarXiwoULIkY9MDCAkZEROavGrP6DGMZ9pc8K9129XsehQ4fwn//5n3JuWKrk9XpRr9fx2GOP4Xvf+56wNxwOh+iscR+EQiHxeTnPTqcTmUymyaax5Mlk2mXEHD58GIcOHcLW1hb8fr9o+vAOJQvKarVKSSjXnjaT8gu0HWzffOvWLczOziKRSMDpdOL555/HxsaGCF+zGxn3qU7c3Gs86ATyfsG4ThaydJvts8kYY9KHgP7W1hZSqZQE7Zw3/jzZvwSeNXOjs7MTNptN1oHsnXq9LiWAJtOe1lRXV5eUI7W1teHQoUO4e/curl69KmwplhTSTpDFeOrUKayurqJQKCAQCKClpUUAIiZBGSfxb53UdbvdUlpUrVbh9/vh9Xqlax0Aia/oa2sgQAN+93uQgDA2NoZbt25hdXVVSnfoczLeAZpjCMZjfFZ+H8C+dwaT/RbLruRJPp8XsWgyqHWMAez5s9pv13FfW1ubaPvx/fkzlFvJ5XJyjmw2m+hY8TX4PvxbJ3jr9brEkUbGFJ9X+2KMd7e2trC+vg6bzYZgMPixz+J9Z9TwQXW2TGfA6awvLCygp6cHnZ2dIpJZr9cRj8eRzWbhdrul5KlcLmNpaamp84EuZWBQWavVhPYkH/Bnh2RjY6OJPsXLlBmL1tbdVo6NRgOJREK+73a7JXgPh8NwOp2oVqtYWVmR1+f7c3H5PtzMD+Ni+2WGblnHjDXBJFJFGRQHAgFUq1X4fD5EIhFkMhlUKhVxFviH5SfsFsX3IJrMr3Fe+Hqk8RMp5fqw3p9BCRXbaZg538aMLF/HyCyw2WziTDOLz4uAB52tEJmB5BwRbGRJHrtm8H1YosHxIIJ7fZ44NEBDI8S9r5+HxoSgk2aj0ABqui9fi3PNLB4vDJ5jncklas1yCL1mrBfmszDw8Hq9CIVCKJVKKBaL8Hq94rCSycPX4mBAAUDANP48M6CkfzcaDRFC4yWpwUIdLGmkXL/XJx2IcDBYPn/+vHTeCgQCYgsJwOkMENDcupDrV6lUsLq6Ktksj8eDzs5OWTvOOUHNy5cvy+86nU65IJn54FoXi0VcunQJq6ur0hUmmUzuqydFPZNEIiHdOOgk03nlXiD7hBnrgwSe3Wvw/JC1wi5LtVoNDodDWu6ywxqZNHS4fT6fBOoU9PZ4PPD7/cjlcrLm6XQay8vLSCQSCIfDOHr0aBMYHolEJOvKshiuGfdFtVrFxYsXcefOHbmTAYjGx5kzZ8Qm0nll2QUBANqS1tZWRKNReDwe3Lx5E319fYhEIk0AurZbvwpAzX7DZNrtUjkxMYErV65geHgYfr+/iZlJCjedTwIkLNVgYomZZia2WBqo9Zn4ukyIcH+4XC7JbtpsNmHe8LwRXAAg5aKbm5tSQsdOctQi8fl8TULsvBsSiQRWVlZw9OjRj+iCfZKDn83lcuHVV1/FD37wA2EhHDlyRH6uXq9jamoKy8vLKBaLMJvN6OjoQDKZFJ9RM0dNJhNGRkYQjUZx/fr1JjYxWTdmsxk+nw+bm5uit0GwE9j1DYvFIk6cOIGenh4BsrUORX9/v4BmxWJRmKe6rJd6JQSpKSwbCASagiEyOSgQzGSI1+tFW1ubJEK3trbgdrvxgx/8ALdu3cJf/uVfwmKxIJ1Ow+12o7OzE9/85jeRTCbh9Xrx5JNPSmab5/hBBYi/aHDPMQjyer2w2+1YWVkRGQUmh5xOJzo6OlCv13Hp0iXkcjlEIhFsbGzg0qVL0qWnu7u7qWS70WiI3g3vIILdZF/XajV86lOfktLuYrEIl8uFxcVFABCdG/qburSYQDd9KovFgpWVFbkHbDYb+vr6sLy8jHK5jJGREfT/rEmJ3+8XAFCX5WiW6Sc16KPq/aFBFJbS5HI5dHV1CWslEAigu7sbCwsLKBaLCAaD0kWJDBzqmZZKJQEEyFpjNUa1WsXZs2dhNptx4cIFfOpTn0IsFsOlS5dES2p0dFRaq5vNZkxNTaGrqwvpdBrHjx/H0tKSJEPoSzJhf/nyZYTDYTz//PN46623msqWWYpcr9eFbcMEbqVSgdfrRS6XQ0dHh1QbkMVFn4isKYIi1AikDSdAyM+vgYD7OehfRSIRDAwMYGZmBtevXxcBdPqXmg2i4w4m7fQ53Y8lxxjLYtmVR8nlcojFYsKUIquUNpd3oH5OnnfNMudr6yoA+qxM9vHZyFpkYlInno3MbZ3INSZAdaypYzX65Iy9NjY2kEqlEA6H4Xa7PxlGDbDXoURfIPrr4XAY8Xgc0WhUsncAcPHixabSJb/fjyNHjiCfzyOdTuPSpUs4ffq01Nq3t7eLyBGFwLTwKbAnwLq9vY2lpSUMDQ1JwFav13Hr1i1sbGwgHA7D4/GITg0pcgwW2IWqs7MTjcYutXtxcRE9PT0i/tjb2ys1q8lkEgMDA+jo6DiQjqjf74fT6UShUEAmk0FPT48g+isrK00aLKwJBXYNITsa8JKgQ6eDL84jv869QNSS3b8IBm1sbIizpDVULBaLOK0EeOjA8ODoy1X/m9kl0gjptPCZCejwtRwOR1MZEy/72dlZdHd3CyWSWbBMJgOv1wuv1wuXy9XE4uB4EJREGglN/wM+qsujL0peBiaTSbQwjJcp55UosQ7wabR03THPm9aWYrtmGkxmA5jt4zPzXGazWXR0dMBmswnQFwwGEY/HZR4J3nG9tWgcjR8zyTTMZNsBu63jGTRpZ4msBrJPNICj0fSDANTQ4LPLEsEtXlBcEx34GrMd/BodzZWVFVQqFQwMDEiwrYF0na0lkKsBMLbX5LrydUnZ397exurqqugEcC+xywkDw3K5LOebonnValWACGZAisWiZHU1k+YgrI9xcO9RgJJ7bnx8HB9++KHcO6FQSO5KgvsAJAHBedIOTTAYxPT0NFKpFFwuF+LxOBqNBo4dO4YTJ05I+Sh/h5lC/r4+87SfDocDJ0+eRKFQEIZbIpFAKpXCE088IdlMtgXW9yz3H+20w+EQhygajTaBo3wuoFlI+Fdt0Dn0eDyYmprCv/zLv6BQKMjeZrLD4XAIkMb/j46OIhKJ4MaNG+LQajaA2WyWDDBLaDh3ZA8De1Tx9vZ2BINBZDIZsQPMQHPd+Ye2geWF1NahMLXD4ZC7nq9HYK/RaCCbzQpQY1zPT3Jwvx07dgynT5/G8vIyUqlUU3vd9vZ2DA4O4siRI2hvb0dHRwdmZmZw8+ZNKZ/ined2u1EsFpFMJpsyr2RRA7tnlIzCWq0mwZ8WOjWZTJicnMTnP/95jI6O4oc//CHeeecdnD17Ft3d3dja2sKRI0fw3HPP4dvf/jYKhYKcB62j19LSgkAgAIvFglgsJi2gNzY2xOabzWYBBJnoItOSOjuhUAjHjx+H0+nEzZs3ce7cOYyNjeHYsWNIp9PSMe7u3bvI5XIAdvUZH3/8cZnrB2lzjb6Mkcmm/R3eb36/H1NTU3jnnXdw8uRJ2fP0Sex2O44cOSJlShbLrgh4V1cX6vXdZidra2vw+XzCWCGbjVopLpcLmUxGmKM7Ozvi37PpSSAQEF+FiV4GgR6PBx6PR8B4Mi7m5+dRKpWQz+dRLpfR0dGBRCKBlpYW0aWx2Ww4evSosL34+Vwulwhlp9Pppu5Cej73+/+DCO45NMOAASvtWmtrKzo7O6XMx+12S7IilUrJ/cHzyCCWiXT6fiyDbm3d7ZxIn5aluFevXhWQh/pD0WhU4gD6oNVqFel0GoFAQOLSXC6HQqEgDA+W02UyGQwNDSGXy8l8z83NyRljiWp7ezs8Ho8kNSqVCiKRCP7wD/8QFy5cwNzcHAqFAmq1GmKxGIBdf+7xxx/HmTNnxM4EAgEBbMnGNfqpD2otuX5OpxM9PT1wOBy4dOkSXnjhBQQCgaY7nUlvnjcmRe8Famj/m4MM+62tLfT19TWxB2mH9H7SIA+/TnusS/B1l0xgT4OT+4sxSqVSkSSWcT410KgTE3x/7aMxwcnPppMx9PPW1tawurqKoaGhpmYqv8gPeiBADYeeWADC0BgaGsLy8rLo15BVkUgk4PV6EQ6HJfBbX1+XDcq6z1KphLt378JsNmNsbEwyjHQgdC2byWSS9oxzc3N49NFHUa/XcfXqVfT19WFxcRGZTEYCBi46g1W2XWSHjNbWVqHlxWIxDA8Pi74NDzWZIwfBidlvMIsbj8dFIJBAGjMABDN0yQEAYdsQlWQZG2sAiY52dnaKc7hfSRKZGBpRBiBBuzZMGgFl2ZZuQaoHQRYGsRTAItVYo8Eul0uCDAafDD7oBLFLAH/W6XRKuUE2m0VPT4+U3TzIoSnzHNpwA80XM+dVB/Fms1mofkAzrU+XbGmniPuCIAidEWA3g8eSBoJgRKcZ0HEQMOXghc4AJRwONwkWr6+vi+PMdeNn0HPA1+TlQdYVnzuZTApLQ3cu09RE7QBqSuXDBAGMtsLoYAG7n5GieAQSqbXEixLYOwO6pIafF9irHWZWmc6p7tIE7NnvWCyG9fV1AQEI6jHQBCDiidvbu60vr127JlpBfE/Oqz4vLpdLMovUutFZE5akLiws4KmnnpLPfpBAGuNz8BJn5zQCXF6vFyMjI7hz5w4ASJcIAjNaOwjY01DSzuDW1hbGxsbQ09MjtO7JyUn09vY2sSJ1pkYDeLTFXAtg146MjIzA7Xbj3XffxerqqnQ09Pv9AuLn83nMz8+LZlelUhFmTrlcltfSdpsdgnh2tc3RtORfpaFtRl9fHwKBAGKxGDo7OyXbzXII2u1GY1fkfHFxEYODg+Lb6JILnYzQSYednR14PB6h1zNhwFI6n88n7CpqV9Ae8JySDcDOKV6vF4lEQhiPOzs7CIfD6OjokKBDU7tXVlbg8/lEt453kT6Hn8Q6alvldDrx27/92zh//jwWFxcxOzuLaDQq562npwd9fX2inXTr1i3E43EB9tPpNCwWiwjxsmUzPyt9RJbXAHvaiBsbG4jFYqIvMzg4iFOnTuGll17CxMQErl69im9961s4cuQI/u///g/BYBA+nw8ejwdf/vKXUa/X8b3vfU9EoXmPsa03WTU7OztYW1sTMWEGSgQBLBYLOjs7xRYzQWGz2fDyyy9jcHAQuVwO3/jGN1CtVvHKK6+gXt8tvaKuzdtvvy0SAy+++CJ6enqabK5Oit3vwTuZ4KKR6arPHm3miRMn8KMf/QgXL17EiRMn5K7h6zmdTjmnPI9kRPt8PsTjcZFguHbtGur1upSHhsNhSSTyuShGC+y13iWoE41GJbDlZ7FY9rqHJZNJxONxYUH29fXBZrMhEAiIhk6pVEJ/fz8cDgfW1tZkbjweDzKZzEfsaSqVwokTJ5qkAjhX2q/h3D0oKYb99gQBNa4XQZl0Oo3x8XEUCgVUKhW43W5UKhVsbW0hm82KFh3Q3DBBs9e5/6mZ0mg08OMf/xiNRgPFYhHvvPOOlNGQRUZfiaxEMmbIOmtpacGFCxeEucKyKLfbLQysYrGIrq4uYUyx0oMAuMViQV9fn5QXrq+v46/+6q8AAAMDA3jssceQSqXwk5/8BBsbG+jv78fAwACsViuy2SzC4TBKpRJisRhqtZroGGnAQTNi77fdpd9ht9sxMjKCwcFBXL16FVeuXMHZs2fR3t4Ol8uFUqkk68J7SoMdPC/7xSd6P9IPIIDC+JkJD/6u9hcIiDC+537QjSr4PvwdYgIEUeknBYNB6RpI0FQ/rzE+YCzLz6yFvXWCmb9LIkm9XpeyucHBQSntZ4z28xJX9xWo0YuiJ1UvmNlsRjQaxerqKqxWK9bW1kQbhpfD4OCgZFYpnFetVpFMJkUVnyheNptFLpeD1+sVJJNMG16iVqsVg4ODUrfMulsuPkteyBoAIBkJ1skR0GFJD9kE6XRa1NpZ303BZO3MHKTBbBxb5FUqFdGdOHPmDK5du4bbt29LRoBIPoWatCAig0Id1HNeSVdjYKj1S2jAeaC4TgzONa2NpUYAhGJII82vG53FnZ0dKW/SDBtmGmnsCOZQOHNzcxOFQkFAK4fDga6uLrl0W1tbsba2Jns1FArd8+K7nwZUgzTa2PF9tOHTv8NBh420XQBSf88gWYuOMrDfL4tFw2y320WsLZVKIRaLobu7W4JK/j5BTw7N9CgUCujs7BRAZnt7WzrD6WEEpQjU0QDSKWUZDn+GBlMzw+6VidBMDWOZxic5aEvJqGCLSWBPc0rryvDS0JcbAY56vS6MlZGREQwPD4tNY2DH86lZOefOncPw8LAEDlrDiGNnZ0coyuFwGG+99ZZ8j3PKZ+N+4xnlZ+HFpwffj1RRvX4HYX304Dwyy6qBKoJOo6OjcLvdouPFe6NSqSAQCMDv9yOVSkkmjTYzFAphamoKfr9fzhXPNQA5x5oWTaeF66/3vd7jZvOu4Ojp06cxMzMDj8eDeDwudrReryOfzyObzcLn88HhcCCZTCKdTovGg6YLs4U8xQi7u7ubbMCv6tBALgB0dHRgYmIC09PTeOSRR2TuSevnXBM4KRaLWF5eFkdXd10zzo/dbpd9T8CMAQHnmaV0DPLS6bTYA50o0yUJLHlk5p+Obr1eF2FLZhjb29uRTqdx+/ZtTE5OorOz88CxhDWAPzk5iTNnzuCb3/wmrl+/jrNnzwpDml2bqEPIwCCXy8Hn88l51WV8nLOtrS0UCgW5p3iO6NPEYjEJFr/2ta/h13/91zEwMIB6vY63334bf//3f49kMone3l7RfXrxxRfR19eH8fFx/Nmf/RmOHz+Ob3/723j//fdRKpVgt9ul2xLfk6Vx6XRaBPIZCLhcLtRqNel+w7mhjRkZGUEwGMT58+cxOzuLz33ucxgbG0M6nZZ7cmlpCcvLy7BYLBgbG8MLL7wgEgTGRM6DXk/go5oW+71vf38/3G43rly5IkLYBK9MJlOTv6c7nTJgi0QiYrsY/PLsUp9pc3MTq6urmJycxNDQkPg2OgHJpGYgEMDJkyfx+uuvyx3L92MXyq6uLkxMTIj/yvUj24uMZYoYcw/W67slP2TlUID2xIkTnzg7kXNhTH7p7/t8Pty8eVM0e5iQPXXqFP7t3/5NNM/oUzOxqpMBZNIwYctYcHh4GC+++CIWFhbw3nvvYW5uTgAfVjoUi0X0/6yMrFAoYHNzE8PDwwgGg6jVavjJT36CeDyOUqkkJfkEY3Z2dmC32zEwMIBkMomnnnoK169fR6lUkn3Jc8RSRSYq2OWpWCzi9u3bOH36NHp6eiTxwRI9spzn5+exuLgoWoA6kNex5YMAx3XXx76+Phw6dAjz8/N47733cOrUKdFtzeVycpYUbBVgAAAgAElEQVRYLsZhjEM0eAM0s+e2t7eRyWSQy+UknqQNpv3Sr0t/h3emx+ORpjjU6WN7df279C/N5j1ZBUqbsLEOEyEaBNP7mc/N+0b7PTrxzJ9ta2uThkmVSgUffvghvF4vIpFIU8UP5/2hADWcDB00GoNKfo9ASqVSwfLyMnw+n7REu3PnDubm5mC1WqU0Qi+Ox+OR2sSNjQ309vZKN5SlpSXUajUMDw9LNrKtrQ1jY2PSUYq0toWFBSl/ACAZEx7OUCiEfD4vKs2aSQDsOt8rKyuiEs5Fs1qtknk6iEMjyAS6KOLFIIv1sS0tLVKixmCwpaXlI6VNwF62lo4nALnw+Eej5PybmT673d60zvqA8+s8uPy67jTD/UYjT8OhGSTUvmGJhs5iaqYPQRoAcogJOs3MzCCbzSIajUo2TpfLPKjMon5tI2ptRKs5CC6Rsshgjq/DOWppaUE0GkUikZAaexoSj8cDoLkmlNk+n8+HcrmMdDotpWBsIcm1JUKuDTBZOmQJcP48Ho8wqsjY4Nrw89Ag0nk0m83CxNjZ2ZGa3kqlgoWFBQDNgsHGf+u5Nc7dJ5El3m/w4ujr68PVq1dFnJJOC4NxzUTb7zMCu8ycY8eOSWDCeSZAp3+WGdrp6WlMT09jcnISdrv9I846s08EBDo7O/HCCy8gl8s1ZSUY9BMU5dldXV1FLBYTwIBaURbLbptFv9/fBCjpYPkggTX6YqdQMveryWQSnRqzebetNdeHmgqRSARjY2PIZDLSjY+MPr62LjEzOm8MIDg/3Ad6XbX91L8HAIFAAKdOncL29jZCoZDckWQGhcNhAJBAaHl5GbVa7SMlqR0dHRgbGxP6PoPeg3Ke/n+Hpm4z6/joo4/igw8+wPr6Ovx+vzjpmgXJ+SHlXetW6PnX4DhLXsjIYukZKfFksZKJSHYa34flAiaTSQB1PrsG7wCIoCLLAzQgsbW1heXlZXzhC19oWseDsJba32Qg+LnPfQ7nzp2T0ia/34+uri7xzdiul112WLpkMplEl4vZcQIFvLNImXe73cJ0Y1DXaDQQDofx1FNPwePxYHZ2Fu+99x6uXbsm4tK5XA6dnZ24ceMG8vk8nnzySRw6dAjRaBS/+Zu/iaeffhp//dd/jX/+538WtjmfE9jLdvt8PtljTELQh6LuUbVaFWCCbK7Tp0+LLsipU6cA7CZx8vk8MpkM7ty5I1ouv/M7vyNJSK65kenyoNaUf+8HxuvYorV1t2Xyc889h3PnziGZTKKnp0eYnTrQL5VKuHbtGjY3N9H/s3buuVwOgUAAHR0dAPYSgdRMs1qtcLlcWF1dRa1Ww6FDhyRGSKfTwgKlXxMKhUQodHBwELdu3cLU1JT4Xna7XRp00CdKpVICHPIMam2qTCYj9wdjF4JP8/Pz8Hg8wvD5uHP7IIYG8XgeuVdo17xeL6LRKKrVqoiTVyoVvPjii9LBt6WlBfF4XBIBGsxiUOt0OhEMBiUorlQqsFqtmJychNvtxrlz56QyIplMitbQl770Jbz22mtyny4uLmJhYUESEWtra3Ift7TsdusjQFYoFEToG9j1WRjH8m/qjTEmDAaDAHb9bMatiUQCCwsLcLvd2N7exuDgIJ599lk5d6lUCqurqzCbzdIm2xhX6zvjQZxF3j9+vx+Tk5N47733cPXqVdy9exfHjx8XVib3Pm2EBvGNAAtfVw+SMLRGKdlt+uzrOIcxO/2i9vZ26faqy8TITNSxAwXFuSasqGAHaibySbbQ/pO2S3pfM/moPx8TKX6/X/S9bt68idu3b+PXfu3X0NvbK3Gn8Xf3G/e965N+Qx7a5eVlpNNpdHR0oL29HYlEQpxy0qcDgQBGRkaEpUJnlrXxFM2rVquiEs5WeWxjub6+LgipDhQJOAwPD8uFxZaXbINWLpexsLAgAEY4HJb6QJ/Ph2KxKBc1HRiW/LCchAheMBhsquMzbuCHPfY7HBSiq9VqWFxcxMjIiGzAQCAAk8mEpaUlEdHlOjGrw0wtwSk6jRwUGGbwbGREaOPCzlmsN6XzaNRhIdJLPRId6BkdRz4rhwZjdLkTDzGBAa2Rs7Ozg/7+fqmbBHY1TxKJBEKhEBwOB/x+/0NzWLWDwvfcj/nBr9GwtbW1SXkFQZWenh75TCwVpLCiDkQ45/r1ud4EZvTrr62tCbuFz0vKq9VqRTqdRj6fbwpeMpkMuru7m1pF68HMJwMMnj/9bw26ud1umM1mrK6u4urVqyJczSwx11l/rv3mll9/GGM/Z9T4PbPZjFAohA8//BBf+tKXRE+Ede787HTa+YcODveF3W4XUTs6pZqizDkCdp2jUCiEl156Sewl7aox68ELi+UDfr8fkUikaW5pExhAEuyNRCKSFdEUULL+JiYm5NLUr3eQQBqgmd7L+4uBO7DHrCG4AUDAMtonguIMmHW73ra2Nim74O/qsd+lz3XZz4E2Zom0NpHD4ZCsLwVrh4aGpKOKzWaTkkU6UDzvGkR1u91NZ0u/p/EM/ioNfo6BgQH09fXh7t278Hq94qRrsJ+sBq67vtN43zCRwXPKbDB9CHZEo24N70K2U2fQyvfj3yyP5H1P30qXShAQZClBKpWS9bt48SLcbreUfgMHR79LJ0noLzz22GP4jd/4Dfzd3/0d3n77bZw9exa9vb1NDF6/34+enh7RyajX61LiEggEpE097SJBOfoPDAZozwgEJZNJ/M3f/A16e3vh8/lgs9lED0VngMkifO2113Dx4kW88sorIuT93HPP4d///d9Fh4MBBteUArYM6MnmZolHo9EQjR02BHC73QiHw1JGNTAwgJs3b4pmz/r6OtbX14Xt8+qrr+LVV19t0vS4l691v4fxrtCAjQ6W+LNtbW145pln8N3vfhd3796VUl7+HBN029vbWF9fh9frRTKZlDljST01ppxOp4CftM1TU1PCvGH5GUsXWQKj799Go4Gnn35augoRwGVJFeczl8thZ2dXMJrteulTb25uoq2tTbQuWarBvZfNZnHt2jUcOXJkXz9U21x9Jz3IddMaHZpdCkDY8W63WwBO+o+hUAhf+cpXcPnyZSwvL6O7uxs+nw9ra2vIZDLCfmlvb5dEEX0avm4ymcTy8jKuXLmCTCYDp9OJbDaLVColJS75fB79/f1YXV0VLaqVlRUcPnxYWoMHAgEpQeTe8Pv9wtjg/ZZMJlEul+H3+4V9SN0g2gomf6k9xCoDigQ7nU689NJLsNlsUp46PT0tZVaDg4MCyNLn5Xw+qNInPdra2tDf349oNIqLFy/i0qVLmJqaEtAwkUhIwlzHU3roRJGOBfWgL6lLioylQHwd+phsn05WzMDAwEcSvBzaTyUIR0C7XC6LPpjX623yM/cbGrzXe96YBG5ra4Pf75fP8O6778LpdGJsbEy+/nHX776XPmm0ncYlHo9LcEwWjNfrxcrKChwOh+jQkDZvsVjEAfR4PFhcXMTW1m5bK15evb29QlMibYoBP0Vy2TmKnTNYrkRaNmtDe3p6MD09jfn5eTidThw9ehQbGxtYX1+Hx+NpuqR5IHXGisinybRb40bq4kEdZrMZgUAAgUAAm5ubUvPMjJzZbMbk5GRTmQxRa/4+NyaNFjP7RCl5KDg//HleQnSqCISRpQFAylfo/JPeyNdllohINwMRGkU+I2l0PJAMdLa3t4V2zvUE9jpkUGza7/djYmJCAtnt7W3cunULXq8XtVoN3d3dEvA/rEFjxnnSh1yXPBgNidfrhdvtRqFQwNzcHI4dOyYOAw0jM7XUEOFFZbxsGdwHg0GZW9Zqx+Nx2RPauQsEAigUCkLp5mchpZUXK9AsSKc/GwMT2hldzsVBh4lllcxyj42NiQOlA2Rtq/YLGA9CMKLBTTpkpAeTcUInho45sFeqpimV+ixyXblO+pLkupHN2NfXB6vVitu3b8uZY23tfnafbUY160XX7fIyZIbaZDJJYENmY6Oxqxc1OzuL55577kAyaPYbxuwXsBdQ8vywPJDBAdk1OkC5desWfvrTn+KRRx7B888/L0kL2mEG6ACaHAudFNC2gEMDJkbninabr8GMMrujUJiUwusmk0mCFQqLU0tFA6gMIDVQY1xHI0h6EIcRJOcefeKJJ/Dmm2/iyJEjTSwZbcd08kgHiQwGeV9pTQdd8sAzubOzK8StOwiR9URWnJ5vI5iv7xCdMeQerdfrYo+3t7dx5coVPPXUUwK4HaTB5+bnAHaz7b/1W7+F73znO7h9+zZeeuklATzIBt7c3EQ0GsXJkydx5coVKbFn8NxoNKQcnj6MzWaT71G/h/7CsWPHpH1tIBAQXxSAlEwAe/oKDDYjkQiSySS+8Y1vYGhoCC6XC2+88QYqlYr4mTs7O2LbWSJOH4WafTyH9Nl2dnYkObK9vY3h4WE8/vjjAhweOnQIbW1t8Hq9Uup98eJFxONxHD16FK+++io8Hk+T9hmf/+cFMfdjGEGZj/Pz0WgUjz32GG7duoWTJ0/K98jOKJVKCAQCePbZZzE4OCixAPUWCQBoZhv9TzIlOHfsIEvxdWoWsURf7xsydXg/0zfhHbuzsyuIzDMNoKlpg26Ekc/nxZ+Lx+O4e/cubt26hZdffvkjcwd8lFltDHzv99BsQ76/9gFp94LBIHK5HPL5PFpaWhCJRIR5ubKygunp6aZknGYTkcmRzWbFPnLOVldXceXKFSwtLcnaO51OAd6YVHe73fB4PMLY4Xq//fbbmJubg9m8K2rM0lM2sOF55/oDu1UYLDs1m81IpVLSmSoQCAjTifuCbD6TyYT5+Xm88MILOHbsGLLZLJxOJ2KxGDY2NgQ02O8Maj/1Qa8p/bKRkRE4nU6ptCDI5Pf7kUwmZf310D6Q9i2Nn8fof+o7iXuIQwMhZKHxNbW9ZYJSx+HcK2TvkMFGW07yBhMZQLO2onFejMkwfp33ETUlW1tbkUwm8d577yEUCmFwcFCSIh933Fc0QV+WesJZNqJ721ODggcokUjAarUKVYnCX/y9RCIhGT2HwyEt3FKplGSpyuUybt68CYfDgfb2dmnnWqlUUCgUsLCwgPb2dvT398smCwaDcilOTU2hUCigXq9L3a7D4RCKFzNaOpChU8QaPYfDIdS4g+x0tre3w+12Y319HRsbG4jH4wJYECgLhUJYW1trCvSq1apcYLrrAJ0Zq9UqwqScGx46OhBcr7a2NjFa/DleesZyF40oM0CgIJx2HjQVjU6NppdpLZNsNitUSlLotIbN4cOHBTyo1+tYWFjA8vKytEjs7u6WDPmDHtp50QGAziwZzx5/h2fB6/VKxzICFrwEOWd0ZnUZDI0asAeAUbOIGaBKpYKlpSVsbGzI/GpRxPb2dqGKMkCh+CFBokKhIEAcPwcDGZ2NoKPLddFry4BlZ2cHMzMzAICxsTEBajSYBewBNMYAhD/3IC/Bjzu47xnoR6NRXL58GZOTk8Jco5jzzs6OBMz8XVLl9XkCICAjA0Fg7zLTrBkAkqXlRcnzqoWJaRN5jnVXLb62ZsKRocP9qsu26BSxTM/pdDbpDhzksR8woofJZJJAjaxSrhd/ltnZSCSCUCgkwu7UzqKzox0G7hHubyPY+XGfm+wCJjW8Xi98Ph/W19dx/vx5hEIhnDp1ShxZi8UCj8cjttzogPA1edb1PjR+hoM+NPDFeTaZdrVR3nzzTaysrIj+HQBhabS3t0tQyDtMg8P6jPB3efY0K5XvzUCH2cxisSjZXp4d2kNgT4BS7zP+HO19uVyWZ3I4HGg0Gpifn0exWMRTTz0le0szcQ7iMJl2S0Sffvpp/M///A+uXLkiejG6fMzj8SAYDGJ0dBSzs7NNCaHt7W3k83kBXHifkUWtNWMYTIXD4SZ/QAcoLM2g78PSmq2tLdHHuHnzJq5evYq5uTn4fD6Ew2HpfkcbzaCRiS3uCTKtCCgw0CRwE4lEpDkGk1BerxelUgnZbBbpdBozMzOw2+346le/KokcoLmE4WEANcBe0P+LbBjB4O3tbTz99NP427/9W8TjcYyPj0tAxiStzWZDb2+vlLdpu6Q7pGkdNPqhBMbYJIH3WrVaRTabhd/vF2Y/k1FkMmlxeN7htB/hcFjKrRiA0iYwCOVzUEiXa7aysoLJyUmMjY0B+PndYx5GUK/ZT7zDgeZGGNvb2+jo6MD6+rrEWezMGgwGcejQIayvr2NhYUH8SupnMgEE4CPt4svlMiqVCn784x8LMymfz8Pj8UjnIK4xAS/ep6urq1heXsaNGzfE/kUiEdTru51JKbhOf5bx38bGhsQiBMZZUsUSp3K5jM7OTpHnoIBxpVJBf38/fvd3fxfb29vCWCXDqr+/Xzou6WS0cU0f5N3JM9De3i6f4fbt23j//fdx5swZWK1WdHZ2YmtrSwBsDVTopB+BGH6PjDfj+xn9T11OrdlE/DnecWR68+dZdcF4wujL81n4O7r6Q9/BnGcNHHPO+fs6Kc5n59lmpcjMzAzW19dx7Ngx2Z+/zLjvpU/GbJnFYpGsqM/nQ61Ww9LSEnw+n2T4GVzpbCHZDxTLo+CQ1WpFOByWjjMdHR3I5/Oiq9HV1SWBndvtlmCypaUFg4ODcuDJ1ikWi1hbWxPk3GazoVQqCcJOpojJtCsESKNNQ8SMPhfNarU21RQfhCzhfkEosCuEuLi4CIfDgenpaUQikSYGCltYMnOrg3ACY6yJBtDkUBhF0PQlxMuKHWwYgBEQ4EHhJQXsdTEC9sRoeSDv9Xl5QdAh4vrQMWVWm8Ce3W4XB6e3t1fqZwFIO0WXy4VkMolDhw5J94h7XZD3O/uokWZtNPYzFHx/OgQdHR1CM6WAGY0ljRUdPWNAz/XgOeLXCaJWq1WkUimhmGrWAIXeON8a/NGBYbFYlPPGdaEtoWPDIFUHGTrTrAOicrmMixcvwmazYWxsDL29vRJ8GOeQ/2cWS8/hwxj7sQuMX+dnbW9vx6OPPopvfOMbOHnyZFNnJZ1h0kKmFDbVQJVuscsuMppdlU6n4ff7JfBjza7WGCLdlT9TqVSaWFi04fs5nwRua7VaE01dgz1utxsXLlwQVhuwp9HwSdtUPYzrZzyL+o92OjS4rNlJwC6Tie1AqbfApAcveW0DAHzEifll5ojPQ5YoO13kcjlkMhlYLBbRc+jp6ZGgk/aTdoFrq4fOepIJx/n4JEEa4/t+HHuts5mazeH1ejE1NYW1tTX09PRI4kaXvOnuicCeM6rXi4Eif0c7rACEtZrNZsVGUV+FAY0GuDUIy/uDNpL7lGeKyRU+69bWFq5cuYLR0VH09fU12Zd72ayHPYxgH/eVz+fDSy+9hDfeeAMXL17ExMQEwuEwXC4X5ubmsL6+jmg0Ktp81BxhMMYgkSwVlsGTbUrHX98ltVqtidHLO3B9fR0OhwP9/f2oVqsAdgE8thAmIO5yuTAxMSG0eCZDSPNvNBrC+uVZJeOba0NdGq47QQzaU4Ix4XC4qaXzd77zHRSLRXzhC18QLSL6tzoZ9DDPKgMeIyOMQ/tDLS0tGBsbw/Hjx/Hmm2+i/2eCsWbzbmdYrndLSwvS6bT4mS6XS0qluadZ2sagjfcYGcHMvrNjUWtra5O2ETUddeygfUT9f/qetPHAnkgqS5uZhMnn800dEy9fvoxXX31V7KkGCPXQcdmDHDq5RTtmDNzJbO/q6hLfkDZwa2u3o2EwGMQbb7yBzc1NXL9+XRhknBvGCZwnJjry+TwuXryIJ554Ag6HAxsbG5J4p8+Qy+WaXouNYlZXV6UDE+e9Wq1KOVKlUpEGMwTvNjY2hCVDbSGz2Qyn04lCoYBsNivVHQ6HQxJcm5ub6Ovrw9e//nUMDw9jdXVV4stcLgeHwwGPxyNglF5Tff/o9X2Qo7V1t6362NgYzp07h//6r//C0aNHBeQi2YFEDIJWGrijX7ifDdE+PrDHsOHXaR81Q4d+E2MQ+izlchnlchn5fF6qOzg06KvvSNpWHUsxAa2f917zzN+hPQIgbEY+w7vvvguXyyXdnvT9+XHO5X2LRu7lHBJdXF1dxc7ODoaGhvDYY48hHA4LSAPsGuVarSbBGnVSWLbR0dGB/v5+JBIJzM7OYmFhAel0GsViER6PB11dXXC73dJutru7G52dnfD5fPD7/bDZbJL52NjYQCaTwerqKuLxOAqFghxAUr2140IAh4iuUciRYA2f1eVy7UsDOwhDZ0TYWtnv90uNstYk6e3txeHDhwXFpuNH8Sd9YHQGRDsKAISSrf+QAkqnlBo4zAjpTLv+W2flCaLoz6Zrx9nJqVQqSethfQBpPAi88d/MNmkwYG5uDuVyWWj9U1NTwih4WM6LBmR0ts4I1HAuNEOmo6MDIyMjaGlpQSwWQy6XE/Sf2VZeqswY8gLl+vKSBCDzT7HtVColZ0c7T7oem/RgOpaNRkPOXaPREIFqdlngXtUGXmvV0JHU3VJIH47FYlhYWMDw8DCefPJJAdW0MdXzaswSa5DqYYyfdwlo22oymTA1NYVwOIwbN27A4XCIY0hHgHOvz5nOhOtLRf+ODk58Pp9kmKm9wFbnAOTnmM1ifTep2kbaKS9eOr2ahZfP56UtIjMhXKu5uTlMTU01BUacC44HnVn6OGO/gFEDjUYnXf+e1g/hWrATxIkTJ4QB4Ha7m0ob9XwY11SDY/vNz34ZIKPzwBKnSCQiwUQgEEAkEkGlUhFGpc6C6cyVtktkNe6nefWrPDhnLS0tmJiYkG4TdCLZrYnJAn52nh2eb81SMIIhZAYAewxAq9WKSCQi7w00O4yca/or3IP8m+ePgRLvOjquBCzm5uZw6tQptLe3N7GhDvowm804duwYTp06haWlJfzoRz9CtVoV8JkCotSaO3ToEMbGxiQDSgebf7Nsgr4f7SuDcQYMHMyor6+vY2ZmBjdu3BD9vXfffRczMzOIx+Mi9Es/wuPxoKenR/QcgV3bSWAtm82iVCrJnUe7SsChra0N5XIZqVQK+Xxe/g6FQmhpacGVK1ekQQf34czMDBYWFjAyMoI/+IM/kESjtrl67zwskI62UzPXjH4P/01w8cyZM1hcXMTFixfl7qKmRj6fR6FQQDwex+3bt7GysoJMJiPMbL4nAQ+ejUajgVKphPX1dczNzYn4L/WgeF+5XC5JKM/Pz2NtbU10NGgnddk+34+gG31e7iPajO7ublQqFUle7ezs4Pz586jX6zh8+HAT4KYDej0/D+NuNLI89Xty/7BUl62yGeATLHU4HOjp6cEjjzwicQYTGZw/li7m83nRGzGZTNINK5VKSflZW1ub+KQWi6VJSoH+bLlcFgCOGopsEe73+4UswOcPBoNSnqR9HQJvJpNJYtFgMAiTyYRkMolSqYSJiQm88MIL+Iu/+AtMTExIRQOBPzbpoF3Sc8iz/rDuTq6hxWJBT08PxsbG0NPTg8XFRVy/fh12u12Aw1AoJMCY1jekvp6x2y6w5/NqhqIxpuFdyPtqv0HQ22TalQTIZrPCxGZMyffc2toShpQGYPS9xphPg6vaV+Jzaca5tk9Mct28eRPZbBYLCwv48Y9/DKfTiaGhIWkJbzyrP29d7xujZj+0m8NsNotOjMPhQDabbZqEer0uh5VlDQy4KKjF71EM1WQyiUp4LpcT9s38/Dx8Pp+UI9HRaW9vl5Ic6uQAe22cmbVgPTHVrLlxyLggXY6OGA9pqVSSFo96Q31S2Qg99gPP+JloXCORCGKxGAYHB+UytlqtGB0dRalUQi6Xk5ayvJhovLixGbxp2hkPCYE4ABKY645QDoejCfziM+qMCg8F14zPwUOsax01XVBn4GlsySrgmtMpa29vRzQaRaPRkH1RKpVw9+5dERQ7dOgQ3G63BJ7AgwfijIaL+0n/3xi00dEBIGJbwWAQiUQCc3NzeOyxx6Rrk6bksrxPO/D8nqb75fN5yarzXNBpAiAGulgsIpFISHZKM2u4HiylS6fTTaAa35Ofw2LZFQ+rVCpNNEoGQVTIv3DhAsrlMkZGRoQBoBF+Pa/8ug5WOZ8HMaD0er04e/YsXn/9dUxNTUmHBGb1eIb4bwZZtIncF3QiWY6hy9Jo+7iuuVwOxWJRBJ9Z5kI7SGdRi60TgCBoyzNHm2qz2SQzxkteizJevXoVJ06cQHd3N4DmspCDPPazt0AzIKjvA/5NxglHo9EQrTYGa8bX+3kXvLYD/L/x6/y3DtQ1aEkmAQBMTk6itbUVqVQKhUJBgknNtNCAg/Hz6/N8r7n6VRoa1KrX6+ju7kZraysuXbqEM2fOwGw2o6urCx0dHYjFYhJoALuBPEsZGGwYGxAw+OC+4LnhmSWblQkmDQbyPtN3oH5usl0ByFkPBoNiv83mXRFhu92Ow4cPNzmoB2nsBz6SjRKJRPDKK6/g/fffx/T0NI4ePYrJyUk4HA7cunULg4ODcLvd6O3txdzcHK5evSol+Wx1zTvBZrMJs5vrxUCQdxNZxpolXq1WEY/H8dRTT2FsbAy5XA63bt1CMpmUTjDt7e3o6+uTMgd9XoA9cI7rzPNIbTL+LO0D/aBisYhKpQKXy4VoNIr19XXcuXMH4+PjqFarmJ2dxc7ODr7//e/DZrPhj/7oj3DixAlhcOyX0NDBv7EU40ENHSdoMJLPxEHdwNOnT+P8+fMYGRmB3W7HxMQELl26hFQqBYfDgTt37iCdTuPUqVMwm82S0DMyU3iPEQBgQoo6mWazGd3d3chmszCZdhkRwO7Z5l3n8/lk/fi6Wi+JfixLTfnaTGwVCgWMj48DgJSyTU9P4+rVq/jyl7+MSCTS5C/rO8X47wdta433kU4e8FwyScoYj3uUJaIulwt2ux3d3d3SbZc2UwNBtVpNmAns5Mo4Y2ZmBmfPnsXGxoaUL9KHpd8DQPwSq9WKYrEIn8+Her0uP0/AgeLqtVoNjz32GCqVCi5cuCAC5fRBOb9tbW3Y2dmBx+MRlv7m5paw9mYAACAASURBVKbYFup9lctlLC4uoqWlpQkw9Hq9CAQCTUlKYI+dy3PAtX3Qw2w2i85jd3c3bt26hddeew0nTpzAoUOHRBiZCX/dFVTHKcbyIyMQw/3Bc2e8c+6VbGI5os/nk7bvxnuKMR5jG/otOimoS6uMbBo+i7E0SgO79KEjkQiq1SoGBwcRjUbxk5/8BOVyGaFQCNFotKnsy3hu77kG/18rt88wokN6ULeFlFtOAh0QrflitVpFWJEaJhR/IoJH0IWaFdlsVtgxZNfw+7lcTjIWNBbJZBKFQkFqV+12u3RsokaO3+9Hd3e3COoRmeNlyWw1Bw+Pzm7xb01v/iTGfig3QYZoNIrV1VVpQ5dMJgUpbGlpgc/nQ09PjziRDodDgjQaVc4tO2hxfqiDYywPo2NPmhoPEDURtNAvy6I0EkrGBV/L6XQiEAjA7/cLa4pzbzLtUsK9Xq8weCjgqWuNKUal+9tT9O3y5cuS+S+VShgfHxcU/2EFGjRc+nPp9zY+B88hDQtbxvf/jH5948YN1Ot1eL1ecdRpcHg5aI0EGiQaUwDCSiOFlcaMrAlmlltaWpDNZgU0pQ4AAKGW05HJZrPSwUiDbFojB4DYAV6ovBgJ4vzwhz8UcIp7cD8wi3OnDSYvFl0K9SDHL7OHCIA8/fTT8Pv9mJmZwfHjx5sCFJ4vfq1UKqFQKOyrD8J/k03Gc873oXNEx5QBpXYYGDxQz4kODvcf11Y7rHRk6bTabDYRK6XtWFtbw1NPPSWZRmMW4pedu4c17pVVBJrLXfS9QHYD/+b+bm1tlQuer03bqcsHP05WRg9dTmMMwLW2CdeeTEeTyYRgMCgt1HludJnqz5sL7qGHFeQ9yGEMFtmIYHl5WYCZzc1NDA0NwW63CwBNm6aF+rVN1GvJpAedwO3tbWFWLS8vC1jDDDIH71QKI2qdOTLnmImsVquw2+2IRqOy7uvr63jrrbdw8uRJdHV1fYSldVCGTuzw/9xrVqsVjz76KI4fP45UKoUrV64gkUigt7cXsVgMP/3pT7G1tYWBgQERCna5XPD5fPB4POjr60Nvby+6urqa6OpkDRIY4Rxvbm4iHo/j+vXrmJ2dxcrKCqrVKnw+H1wuFwKBgLRaTqVSwg6PxWLC/uAdyWCFCcTt7W20t7fD4/EIu6ZWqyGdTiMej2NxcRHLy8tS6mEymSQZ+cQTTyASieDmzZvY2tqC3+8XXZS33noLd+/exac//Wm8/PLLoq2yX/ZagzQPcx8YbYi+t7jevJccDgfOnj2LlpYWvP/++4jFYmg0Gnj++efRaDTw9ttv49atW6KFWCqVkEwmRcQVQNM54Vyw9GVnZ7dLZSwWE59mY2OjqVSm0WjA7XYLM4m2lb6YFjAm8+rGjRt4//33cePGDcRiMbmzn3jiCbhcLvF37969i9dffx1jY2N49NFHBZQzlpxy72jm6sMYRkBYA/jAXoMK3mttbW3IZrPI5XLi3zcaDXR1dWFoaEiSv/x9soookq2F1nlG0+k0PvjgAykh5PqYzXvizNRN5HNevXpVumcyJqzXd/WAKpUK0uk0otEouru7ceHCBekARr+pWq1KV2IAErvw/9vbu4L7x44dw+joKDweD1ZWVoQBksvlROh4aGhI/CK913nu7lVCdD+HMSlssVjQ2dmJQ4cOobOzEzMzM7h48SJMJpMkCPv6+qRKY7/7wmg39OfTrD1dBkVfgd/Tv6PPFe86DWLx+9rHouasFog2+j8/z7fUMQSToZrxR+1cMiOXl5fx4Ycfig9H4Xc9Hx9nHe/r6TWiZfxDJkqhUBDKUbFYlMuJQTIdQtLigL2SGK/XK4F0T08PPB4PSqWSHFijwBeNW6lUQiqVEsBma2sL0WgUnZ2dCIVC0mI4FAoJQk5wgLQ2oqU0eDzw/D8dKTIGeKg4DlowwY1pNpsxNDQkIAS1apgZIIgyPDwsyD2dSToQWl9mY2NDhE2dTqfQiDX1TGfWeZC5fnQoaUwZhBPl5trqy47CeQ6HQxThvV6vsAgI1HR0dCASicDj8cDr9QolvKOjQyiOExMT8Pl82NnZEdTzxo0bWFpaQkdHB+7cuYPJyUkRqeZcPoyhDRCAJkOogQY+k9HYUHOot7cXbW1tmJmZwebmJrxeb9PPa0CEYJ7e0/p9qG7PuaYR0wJi2nEkgMDX1rRcXt76PfkcAKQUUYM6mmFFB7TRaCCRSODOnTsIBAKSqdSOjC45MM6rnreHsbba2dTDmEnQX7dYdsVbn332WVy+fBnt7e0YHh7+yKVeq9VQLpebMvL8fr1eF1CGP8esYSaTEeCa77m9vS2CsQQKeDYJrHLN+f48p9Qo2tnZkcBQrzv3L0HUSCSCDz74AAMDAxgYGGjKrBy0QPHnDeO6cegsLvclP7/dbv+IQ8L51I6Pce8SLNF/7nXv6P1BNpzOyjJhwWcFmkvXCNqQMaUBI+Pn5mvoP7ynD9q9+MsO/XlNpl3RxcnJSXR2dmJ6ehobGxvI5XLweDw4dOiQJDzItuDZ59wZxbcBNJ1bgnjs4Mc1p+4Tn0UDPGS0ERCnn8RzZzabEYlEcPjwYbhcLgESZmdnYTKZ8Mwzz4j/o9fsoJxDva/220/hcBgvvfQSnE4nrl69itnZWfT09CAUCmFubg6xWEwyrBTXZdDFUlwGzQQtbTabMITJMK5UKlLGRC2T9fV1JJNJuN1u3LhxA9/97nfR3d2Nz3zmM1KW1tvbK4FZuVyWz8QAhQAEzzTPj9PplNIb7j0CCvw5l8uFZ599Fp/+9KextraGVCqFzs5OOJ1OaUt86dIljI+P42tf+5qwwX+ZAOJhjHvd0/oO0ayxaDSKZ599Fm+++Samp6dFw+T5559HV1cXgsEgenp6pIW51p5hkk5n1PlvlslUq1URt19eXsb169dx+fJlVKtV0aHSWph8frIi6RNzLwUCATnPbDF94cIF+Hw+PP744wJeJJNJfO9730Mul8NnP/tZhMNh0W+hPdEJOm0THsZa6gDdyGY33n8tLS3SzIW6hC0tLSgWi8hkMmhpacEXv/hFjI6OSnkU7ypWRCSTSWEkkWXo8XjQ0dGBTCaD6elpYU3Qv2H5Nn1MroPJZJJOptvb20ilUk3aJ62trVhYWMA//uM/CktZC9hq7Sk2nuG+Ymn32NgYPve5z8FqtUqXRA3EMv6kZhL3/n5x9cPyT/X7+Xw+DA8PY2hoCI3Grq7V3bt3xXayoxFjdz6/Bkzu5RNpZg33DO827iedaDSCQIxh+TNGoJlnQScfea6NjHq9j4Fm1gt9M2CvNJF7kl2+WltbhQX+/e9/H6+//jo2NzfR398vchM6YfNxxgODWTVQ43K5EAwGmwI0nT3iRcOAS19SvCitViv8fr8EY7FYDJlMBiaTSTRpIpGIdMlwu92irZBKpZBOpwUA6OvrQ39/P7q7uxEKhRAMBiVrsrOz0wTy0EgyINUAA59XX2oUO9UInc5efhJjv4wrDXkwGMTU1BSWlpYk47O2tiYbmRt/fHxcVMgZ3NFIkqbJbBywd5BowJhBYEaZ80X6MOeWB4EGiqUYFKqigxMIBBAKhRAIBEQ1nkaVAA6DfV0XzDXr7u6Gx+MRCjpFnrSA3urqKi5cuIBwOIz5+Xm43W4cO3asybl+2NklDcwAzYKimrars0+cb9KAA4EAVldXRcCXXWV0YN1oNOQiI6NBXxDaeTKZTE1ZQK39w7UgndPj8Qh7gg4vAU/d0YeG0/h9YyBK8JDvZTKZcOfOHVQqFREb184n/2gjrLNA+mL5ZQzpwxzMKB0/fhzBYBBvvfUWRkZG5GLjuWXGiT+vSwP1XGiNIIKium2hxWIRENZkMmFpaUk6hzFAicfjwqwiCEB7T2o3L0Z2aKDTq+muoVAIhUIBsVgMTz/99EcydL+qAT7PDOvgK5VK06WvAYz9MpEaXAcgtlTbAmYfOXTmyUiXZokqv6cdP5ZR6GfWr8Mzw3uRz8nXMGZ3tZ3Qa/eruI77Ddqher2OSCSCo0ePIhaL4e7du6Kl0N7ejqGhIQBo0pHSVGod4HCu2EWRd5vVapVmB7rLFvcOfStdlk1AgX8IFBHs7u/vRzQaRbFYFHbym2++iZMnTzYBpcZ1PQjDuK/1HmPi54UXXsAXv/hF5HI5fOc738Hy8jIef/xx1Ot1zMzMSMDU29srmgssVVhfX8fKyoqAmQRxstms0OgZSAKQZJHT6cTW1hYSiYQIiyYSCRw5cgSjo6MAAL/fj76+PrS3t+P27dtYWloSO8pzx8SEtt8MPIPBoIgk0+ZT1PqrX/0q/vRP/xRf+cpX0NbWhitXrmBhYQGnT5/Gzs4O5ubm8N5778Hn8+FP/uRPcOzYsaYyi/2G3qMP8+z+omSGfmaehUceeQQ9PT34j//4D2mIkc1m8cwzz0jnJzIXWbbhcrnQaDREWFozbMiIcrvdwhwPBoNwOp0CurNzKO9PaqPU6/UmVgvPktZ67OnpwalTp/DMM8+gv78fPp8PZ8+elfUvFAo4d+4c3nnnHbz88ss4fPjwR/wavrY+Dw9zrTRQZPRL9Wfn91paWtDX1wev1yu6SuxmxWYxf/zHf4zJyckmoW5tCwlSsUuw1+tFZ2cnBgcHYbFYpBsR9XBqtRqGhobg8XjkDiYBgJprfr8fbrdb3ov6KrlcDiaTSfxWJizoJ9Pfok/E2GljYwORSAR//ud/Dq/Xi0qlgvn5eZjNZpTLZSQSCbS0tKC3txeDg4NNZVTG0iA91w96LTUoYjabYbfbMTQ0hJGREZhMJnz3u9/FP/zDP2B9fV32e2dnp5QA6zt/P39GvzY/q46p9V2owR5jjEPQnCxWfm2/xigAJGGhiRfGRNW94gV+Jj4b7UWtVkMoFMLOzg58Ph8cDgfm5+fx5ptvwmq1Ynh4GF1dXSLp8suu333v+kQjpD94e3s7XnzxxY8EEgCaxO10y0pOHp176ing/7H3pcFtntfVBwA3AFxAEtz3nRSpjdooWbuleE9ju96SxqndJM4ynTQz7WQ6nWnTP51O55t0+qczTZ1MmtRuUrveV1l2ZEu0LGu3JFKiRIr7BgIkCIAEQBL4ftDn8uIVSEkOJctunxmNJCwv3vdZ7nLuvecCgpyx9IaGJZHPubk5DA8PY3BwUFJ8+Rsk/IpEIpIxw/+z3WVJSUkMAk4DWTuLPDgawaOTrxdbbzQjOvp5DY0Mmkwm1NbW4uDBg+jo6EBBQQHa2tqQm5uLnJwcUSZ2u10IdEdGRmK6OjGLhugi07zJnk90lMg3M2W0o0gCQ3JccB+xTInGFAnzKKDn5uYEPGJ7UYJ+jCbSySSZLQV8dnY2xsfHUVdXh9zcXBECJtM8/9GpU6ekbvbYsWN46KGHkJGRASAWDY536JbboDUKO50doaPnOvJOocJ9Z7PZJJo4PDwMr9cr/x8ZGZH9ymsw48Lo6HG/szSMkSRjaQ33mG55qqPvBHW00OSa6owhXtNms4my5fkjsJuTkyNC+uDBgzCbzSgsLJToBOeIMsQofPU838yIxWcZjPqVlJRgz549ePbZZ9HS0oLi4mIMDw+LQU8DhSVNOk1a7xENipKHgeSYc3NzwqXB+WYGGh0XHfXgejCSNTs7K+ePYK2+vla8VqsVNpsNb7/9NrZv3x6TJaQV8Bd1cF+xO482bPl+vMHPaXlqNCAsFovUwuuObvw+5YWOItGQ4fnmvtL612ye55fT5Wf8o+WR1vvaUfgyDy1z+e/Z2VmsXbsWra2t2L9/P/Lz8zEzMwOPx4Pm5mbYbDa43W7RhxwMWBjnT5fp0g4CILpXZx5q0IwRRZ5N2l4MerCbDLOTh4eHJajyxhtvIBgM4s477xQ+FONz3iqDckHLBh0IMpvneYIee+wxjI+P48KFC3jjjTfw/e9/XzJe+vv7kZGRgdzcXCQnJ6Ovr0+cL2bTMIu2u7tbyHzT09OlrJZnwul0AgBcLhcASNYx3+vp6cFbb70FYJ5Qle1/GcGnMU8yTNqn/A2/34/Z2VnJhGWQIjs7WwKLly5dwvr16yVouG/fPhw8eBCNjY0oLi7GmTNn8Oabb2JoaAg/+clPcMcdd4hdo4dRHmkg7GYDNQBidBbvL57DR1n4wAMP4B/+4R/wwgsv4C//8i9RW1uL7u5ulJWVwe/3C1jDBhIs/e/s7JTAMHUoSwOTkpLg9/thNs+XujmdTlRUVEgE3WyeJ5hlOQjtKe2DMOOC55pBT1IBzM3NYefOnTHr/e677+LAgQPYvHkzvvKVryA1NfWKgJlR7t5sUE0Hp6+mqwleORwO1NXVCW8QfaiBgQFUVVUhJSUFjz76KF599VUMDAxI9gnlF3WO3++XADu5+ChPafvTHi0qKkJPT4/IXKfTKZkOgUBAyud5Hvv7+8V3JDgOzAdLaMfQbtI269TUFAKBABobG/HUU0+hqqpKshUBwOfzCW9SYWEhampqYq4PxOdGuZnnT9sOCQkJKCoqQnNzM7xeL44ePYpDhw5hbm4OP/rRjySbJCsrC5FIBL29vTGy2Ais6mfQdij/Jp+hsQQr3t4iKK+DSrwW14PgEd/nNY3BIy1T9Bkz3i/XweFwoLKyUjg5HQ4HJiYm8NJLLyEYDAqXT2lpqZTbXe9YFqtXpz/z5jUaZTKZhGeGUVs6gcCCMUGjg5PCDAimHA4NDcHlckkGDZUauwSFQiGp2e3r68Pw8LDUgXs8HomOeDwe+Hw+9PT04PLly1IaxbpFlmOxjpULRUOG2R80mo1RRW6kxRb5Vhj68DidTmzduhVtbW2Cbh84cEAid3Ty2Oo4OztbNiUVT0JCAvLz8wWJppJiZhRBHDqE7MRFclkdEdRZExTK/D273S6lccwCMWYRmEwmqROkwUpBp8tliouLcd999wlhJ6MxXq8Xhw4dgtvtxpo1a/Dxxx+jqqoKVVVVVxgri63rcgtTjeQaIxZaeGklrT9Dx4y1kqFQCKdPn5azSIdCA48sjdD18zqKrp0BOuFaoVBIcv2pnNlSlA4DFRHX0JjKTEVLYnGmylIYcy9ZrVbhCEhISBAgl/NgJGPV96jnUYMDOkPhRgydPXEtQytOYJ5/YMOGDXjzzTdRVFQkjhXrcZkZox1yYxSOoJzdbkcwGJRoIh0EAELSPDc3JzwJbCdJY5fPw3VjJJ/Kk3uLCpM8GqFQCJmZmSgrK8PBgwdRXFyMbdu2xZTDEfzRcovzEW+ObuXBtPhrkRH8jK6njid7uH9TU1PFQaNMYASehI08n5xTDf7wWsBCtoImgzYCxlrOfNEBms9qBPM7LOl1OBy455574HK5cOLECSQlJeHYsWO4dOkSCgoKREfSQTPKb30PjPjSXqKNxA57wELHTB38YoSTOlpHZBkwqaioQG1tLbq6utDV1QWz2YwLFy6gra0NDz/8MJqammKyLOI5Xp+H464Hz4UuL2H0nWtisVhQV1eHvXv3wmq1oqOjA+3t7di+fTsqKysxMTGB1NRU1NfXS1kpOQz8fj/sdrtEbIEFjh8Gfli2RAfR6XSisrISZWVlwtvgdDphs9lw4MAB9PX1SYbH0NCQBLd4/9rhYODC4/FgdHRUMqJYYkXOvkgkIpH4gYEB9PT0ICcnB+fPn8f7778vAanh4WG8++67GBwcxB133IE/+qM/EiBeB2Xi/SHwzvm+mYPnwxiBB2J5IPkcVqsVDQ0N+PrXv44TJ07gF7/4BdLT01FSUoLVq1dLAE/bkH6/XzLKw+EwvF4v3G63ZGU4HA7J2rfb7WLTjoyMSDYFiW2zsrIwOzsLt9uNgYEBXLhwQa7DLCzy9fn9fgQCAYyMjMDtdqO0tBTr1q2TM9/a2ornnnsOjY2N+Pa3v42CggKRFfGyJfW+v5nnkvaz/k1tb8Qry7VYLMjNzUVNTQ0yMzMlCD8xMYGenh4AQElJCe644w44HA4pLaIMi0aj0pyGvhoA4Rol8EN7k2T4WVlZ8Hg8GBsbw5kzZ6SjGzBfvpSQkIDGxkZEo1EpndOBCtqSXLPZ2VmRtcA8CJOYmIjHH38cP/nJT6RBycDAgJTx+3w+WCzzHZXq6+ulI50OwhjteR1IBG5OWZvW8zabDStXrsSqVauwevVqFBYW4uTJk3j66adx7tw52Yfs1GzMAtKYAM+tEchhwF4HVePJIuM9UsfpQBJfJ+0C/Q0gfqdqbRcvNfR5czqdyMrKgs1mg8PhgNfrxTPPPIPnn38eoVAIxcXFqK+vj2nUc91rcN3fWOSm46UJGSdCO996Mmncp6amSmYG2cC1UZiYmAiHw4G8vDyYzWZ4PB5MTk4iHA7D4/HA5XJhZGQE/f39GBkZESeUUQ92FmGLQ5/PB7/fj6mpKRG27HdPAik6akTA+byao4bGr948WplR6X5eEXrj+sSLlKxatQr5+fk4deoUVq9ejdnZWZw+fVo6AvGApKeno6mpSWopNWBjjM4yYkCStYSEBClVYhQjNTVV9gWNH36fDoSu/eX6MMWQQA7TkNkJwWw2Iz09XcptmC2Qnp6O3NxcFBQUID8/X1qx8/c8Hg9aW1vR09ODuro69Pb2IhQKYc+ePeIAcc6u5c9yDQ0A6nO1mGDRApACb2ZmRtYvMTERhw4dgtfrhdlsRkFBgXTh4Wc5v1Ss3NN6bzNqoT+rUW1tSBsj7hoc4/UpP3T2h0bH2SmDe46gHyOLra2tGBwcREpKCpxOpxivxkweYOFccmhHWDuzN3IYI2GL7Rv9nv5OWloa7rvvPgDA2bNnhXMKgCgl3ZKSkXzt+BM00ddni3oy+FMW6+gOOb+4L4GFbC/ycxAc0PcAIEbuJyQkoKKiAp988gn8fj/27t0rgJNeky/LMIJt8d43Pq82bgh+auONnzGeH4LjlJ3sqkX9FY1GhWia50wDsABiujgAV3Z24us0nD8vPXezh9anOliVkJCApqYm7Nq1C0eOHJGo2vnz55GYmCjtzvW8cW0JSDPqzuCQTvcG5g3hmpoapKeny9kFYrnFCGzSoZybm5PS7czMTDgcDly4cAHvvvsuLBYLPB4PDhw4gA0bNuDee++NKUW+VUc8wFLLU95/RkYG9uzZg9tuuw0DAwN46aWX0NnZiXXr1sFms6Gvrw+5ublYuXIlMjIyhAOD8o1zCECc9ry8PADztiG5LphB6HK5hF+R30tJScHKlStRXl4uLXirq6vFoSktLUVTU5OcQxLyT0xMYGhoCJOTkyJ/g8Gg8FyQhJUByJKSEqxYsQJDQ0PYt28f+vv7cdttt6GiogIvvvgiPv74Y5SUlOC73/0uSktLY8CPeLYi97Yen1dmoxEcNso+Lf+sViu2bNmCO++8E6+88gpeffVVZGVlISUlRbhfyMfG8nh2CuI13G63dKykA6m59mZnZ+Hz+dDd3S2EsMwyZaaUy+WSJgkEZWhfsWyKe6WlpQWNjY2SkXHixAm88soryM3NxYMPPoji4uKYuV/MHr2ZmTQc2n7TmQzG82kE3CwWCzIzM1FfX4+kpCR4vV7Z8z09PTCZ5qscHn/8caxZsyamrNvtdgtATr+AgCJBbtr2wLxd29HRIT5bRkaGZA7r7LWioiJ4vV50d3eL/iPwrRsrsPRtYmJC/Bafz4ecnBz85Cc/wVe+8hUp8+/t7cXly5cxODiIoaEh+P1+ZGVloaamRjKUddYM11ZXoGhbSNtiN2uYzWbk5ORgy5YtWLlyJRobG5GZmYlXXnkFf/3Xf40PPvhAnpechhx6r8YD/42ADcFvfp72q9EHAXBFwIlrocEeTdWhf9N4TozyxSgf9fdIHcEEhImJCfzqV7/C008/jWg0irKyMtTX12PdunXIyMj4zGdyWbyQeBkj8QRoJBKRGmBd3sCyiZmZGUn9crvdQvBFo4Y1pEzh7e7uRnJyMrKzs8WRYP012+LNzMxIO8Xs7GxkZmYK/wyjIampqZI2npiYKIKUZT9sMUzAh6CAMZrD9EXjYWNKOTfPzR7xgBnj/zMyMrBt2zY8++yzyMrKwu7du3H27Fm0trZi06ZN0o7aZJrPjqqvr4fT6cTly5dlTXgQmIXBTAoCJ+y8xAwcAEKoRmOIAjApKSmmjR7vmUSk0Wj0Cj4MYB44YPbT7OwsnE4n5ubmiY6Tk5NRVlZ2RS973n9/fz+OHTuG0dFRrF69Gi6XC+3t7fjGN76BvLy8GAFjHEYHermVpD5PS31Gv0+BpBW3zWZDTU0NiouLceHCBbS3t6OlpQXRaFQ4ewDEZNfQmSaoorOYmD6qSX752zq6oxU4o0l8nwre7/fHEE/TyNbCltdmhgfPZl5enmSCMaW2qKgoxsnUc6gFsXFOv2jAQGFhIR599FH8z//8D4qKitDY2IiLFy+KLNLd6giezs7OxpCEUumzpajZbBZSccoukl0yC6azsxOZmZkoKCiQDDdgHiAaGBgQMIFZACaTScAjznFOTg4aGxtx/PhxnDlzBg8++CBKSkpisjeuBmx8EYYGsxYDzI2f1/uVss64lzl4LS1/dAaFzmTi9/k+uyzq76ekpAhozhRkDRIvJet4poGbn37/eQ5tJCYlJeHuu+9Ge3s7Xn/9dXzjG9+A2WzG5cuXUVlZiWh0nvScWRh0HFiGrSPlzEQEYtO/2eWH51vrRmAhI5CZkcnJySguLsbMzAxyc3MxOjqKV199FTMzM8jJycEbb7wBr9eLv/iLv5ASHv1ct+LgvtX7S2cbAAsBg+zsbHzzm9/ExMQEnn/+efT29uLxxx9Hc3MzBgYG0N/fj4KCAlRUVODQoUNobW0Vm47zSr5E6p2+vj4kJiaivLwcAMS2ZDYhgfL8/Hw4nU6sXbsWNTU1OHfuHKanp3Hq1Cm0t7fDbrejtrZWuk8xK4qlVzqYQXuY5eM2mw1TU1NISkpCcXEx7r33XhQVFWH//v3Cx3P//ffjnXfewYcffoiCggJ8+9vfxsqVK0W/auCcg3svO/5owwAAIABJREFU3ln/vPaDDlYtFtDg2SF3ycMPP4zOzk48++yzKCkpwaZNm+DxeAAA77//Pvx+P9xuN8LhMHp6eoQPaHZ2Fh6PR4KJtN9Jc0AAIDk5Genp6cjLyxOC3EAggOHhYYyOjopvMTExId1oeX+zs7Pwer2wWq3YsGEDotEoLl26hMzMTJw+fRq/+tWvMDMzg7/5m7/BmjVr5CzGczA/T5CGQ+sXroUGZPR51dnK9NlWrVqFrq4uyawhYF1WVoaioiI88cQTyMnJwdtvvw2/34/09HQ4HA643W7hf6EfZzKZpO06EwFMJhOGhobERqTcpT/A6gGWInK+MzIyZM8zG8fn8wkXFUnIAaCgoABPPvmkZF4lJyejs7NTgNbx8XGYzWaUl5dj5cqV4qsa10wDCvRrOcecw5vpU/L+EhMTUVJSgh07dohdPz09jYGBAfzsZz+D2+3G9u3bAczbFvQJ9dD7wpjYoTO5OQi8Tk9Pw2QyxXDFGkFb7ffQruX1aUfxmpxDneXF72q9p2Ug/yamQPC2vb0dv/nNb9Da2irZd83Nzdi4cSPKysr+IHDb8tOf/nSp95d8kzdNwQPEKk7joeXrVDxUDBSKNptN0LhwOIyRkREBb9iWjQ4GiZy44Hrz5uTkxIAqzJRhehKwUK5BkIbpxVarFdFoVAQ3uVhYWkHHleUXetEtFgvy8vLE0dGCk3NxHemIf38tH7qWEYlEfqr/v5iCS01NRUJCAj766CMpHenp6UEgEEBmZqYIPEYNMzMzkZAw33qZYAmdNUbjGIUgN4zJZBLSUZ3exvIJRnmZMcWoGMEb3iujwikpKZIVw7UB5h0NIuYulwsDAwOoqKjAihUrBBXn+kxPT6OtrQ3Hjh3D7OwsVqxYAbfbjdbWVuzduxdr1qwRwaH3dbw/xvk1mUzLtY4xa6iFmz5fxjRYADFCj/uyr68PbW1tCIfDaGlpkbXRrc+phBgtpIECLJQrUvgRFOU6MELB/c/zwnvgfTI6RUOI68xnoHPJ61Lwzs7OyroD86U5H3/8MX77298iISEB1dXV2LFjB/Lz82VvEgTSAl7LJxqmxtfNZvOynUXjOmpjeCm5sNh70WhUOuVZLBa89957aGpqQkVFBYLBIMbHxyXizt9g1ImGP88Ns6IIfhLIpuE7OTmJpKSkmG5PoVBIIsI8u9PT0+jt7ZVolnY6LRaLALoZGRmoq6tDd3c39u/fj/vuuw8rV668wiBZak7iGavaIDSMG3IWr2UYo0X8t/EzRl2pP2eM5sT7DWNgRJe96O8Y9nfce9ZnUt+b8TrGe2CkGVgAW43z8AeMZTuLoVDop8t1LSC2PJX6NDs7G2+++SYsFgs2bNgAn88npbxMdWf2EwMTXDsNnvEz1I1WqxWDg4Mil1nKAcRmLhOsYXSvpqYGVqsVw8PDePnll3HixAl8/etfRzgcxosvvoivfe1r2LVrl8jleCPePrja0J8PhUJIT09f1rPIPa+JlTVIyD8ZGRlobGzE5OQkWltbcebMGSQmJmL9+vXweDzo6OiQNrw2mw0DAwMSWHA4HKLnyKvHUhc2r6CjSOdkamoKBQUFePDBB1FRUYGkpCQMDQ3h7bffxrFjx+ByuZCZmYmKigqYTCaMjo5iYGAAgUAAKSkpQkrKgBd1M585GAxi3bp1WL16NdasWYO77rpLgKYjR46gpKQE3/rWt9DR0YF/+7d/Q0NDA/7u7/4OW7duhd1ul/2k960RJI637jdaLy41jDIynnzhmUlISEB6erpki7/xxhvIyMjA2rVrkZubi7S0NCnZ8Hq9aG9vR0lJiZQV9vX1ISEhQVpJU2eSUJqt0Tdt2oSSkhIUFxfDarVicnISIyMj8Pv94oeQMoD2Lwmr3W43duzYIYBqU1MT2tra8Otf/xoulwvf//73sX37dsky1TJbZ1cbSxyvYyzLOs7Ozv6U92IEd437y2h/cZArxufzScUDs5zowzU2NqKyslLKtMlXQ1vQZrOJncpgA99nBrjmsWFX0JycHLhcLlit1pjABGUKM+6j0fmM4/Hxcbn37OxsbN68Gbfffjsef/xxVFVVCWdOZ2cnvF4vxsbG4Pf7AQDFxcVYvXq1tLMGEKO39TxxH+vPGQG5xMTEm3oWWbFAbkj61cPDw2htbYXJZEJTUxOsVqsE5+OBIQzm8dl19oq2A6PRWEJ1nYnP6xAf4HwBV54P/dtGH4q/ZVwPbVfRVjabzcJrZrFYcPr0afzsZz/DuXPnhJply5Yt2LVrF5qbm2OoXq4y4q7jsgA1NMo4GVr483V+loeExIjJyckSuU1LSxN0MTU1FYFAAH6/H6mpqUhLS8Pg4KAga7pcha8xFY2tnKkwaQSRh4F/kzkcQIyjwWsRiGEmjlZoZrNZ3mdWEMn5jIRQnBdGNq8RAb1pQI3ekAUFBQiHwzh16hQqKytRVFSE0dFRuFwuAVy4UUm0m5OTAwAxTh+dd93R6dN7EWODh4zAiXbsaXimpqbK7yUlJUlXBd0liiz7wEKGDteTYM2GDRuEzFF3gxoaGkJHRweOHz8Oi8WC1atXY3BwEG+99RbuuecebN++PYaEivNlmF8RCBoI+HRvLMs6RiKRny7muHI9Oafck8b3ucZ0ljs7O9HT04OqqiqUlZUJeMVWzAReCKDpfUNFSJBVAy/aoKAQZPcuHZXXkZapqSkBBniuOY/6tynYCdQwUjo7O4tf/epX0i5w9erVWLduHRwOhyhyXkc7sfGcX21kfKoob5gSNAJERgNHfy7e4HcsFgvy8/MxOTmJjz76CI2NjSgtLcXMzAyGh4dFZhFY1/wwBN2YTkyCPGbe0NmjYUNAOycnB7m5uQLI6gw6YKFmXT8fr5meno4VK1YgEAjg9ddfx86dO9HS0hLDzxDvOa/mJGqlHWd8bkANh95vRiN1MedoKWcp3jACY/EirDSaFjPqDfv/qqCO8f50ebN+NuO/P8NYtrMYDAZ/yn8vA4B0xTVY5pSamornnnsOiYmJ2LhxIxISEjA2Noa5ufmui06nEwkJCUKUTqBb21HMFKXjkJqaivHx8Rj+J/6twWzK8IKCAtTW1iIQCKC1tRUvvvgiurq68Nhjj6G8vBy//OUvkZeXhz/7sz+L6VYTD4j7rHNzI4Caubm5n2rwU+9VY7SVz0Lyx0AggCNHjuDy5csIh8NYuXIlvF4vjh8/jpycHKxYsQL5+fmwWq3C45ednY1wOCzR+2AwKIEMDYCT86SsrAzf/e53sWrVKphM8w0Pzp49i66uLglYpqWlISEhQdpFU047nc6Yc1dYWIi0tDTMzMxIuWJiYiJ2796Nr371q6isrMTU1BQOHTqEc+fOoaSkBA8//DDm5ubw9NNPIz09HT/4wQ+wbds2uWfaoosFEOMBIZ83UMP7iHfPXGNtZ9OpdDqdOH36NPbt2wcAqKmpQUNDA2w2Gy5duoSPP/4YLpcLK1askOtQV2VnZ0uZWXJysoB6LJkrKysTElW/3y+lTWxwQQeS+9Tr9QoP3K5du8QurampwdmzZ/Hzn/8cbrcb3/zmN/Hggw/G8FEBC+ecc0F79zOez2WzUY3ynjJMgwv6vBp9xMTEROniOjU1JRQYExMTUl5kt9tRXFyM2tpajI6Oore3VwJIBDVnZmbEOWYDBGaJMqhFh35kZARbt27FHXfcgVAohN7eXkSj8wTQrLAgoTSBuqSkJMliq6+vxx133IE77rgDK1askNbt5IoiGMcMnbKyMjQ3N8PhcFwBknIujMERDuOe57zebKCG95mamoqCgoIY3yIUCmFwcBChUAhVVVUxFBc6897oo8ST1dqf0bauzrJnEJZ2qs6o1AA+g8XhcDgmI8eY6MH70OV7xoST1NRUqQQaHBzEL3/5S4yOjgrfUEtLC3bs2IHVq1fH8GQuNlRw5sYANfrA8WGMRqA+pHyNBGgk7GVqFIliiV7zcLJ7i8vlihECdBDM5vkaMU06yQgxQQJm7NBp5O8D84KPbWMZSSTRH7NztKPHv8PhsJDcTk5OIj09XcoygIW6ff3s18h7cVMzaoAFHiCWNF2+fBlZWVkoKiqC2WyOiRDoDAWr1Yr8/HyJEOo50pubh4ZADdFt3f6Z5Hm9vb3o7u4WcCs3N1cEGNNIuT7JyckYGRnB4OCg8GpkZmYiJycHNTU1WLFihdT2cn9OTEzg5MmTQqibnZ2N6upqnDlzBgcOHMCePXuwZ88e2SfX6qBoJfTpnC6bElzKQdPCjUa2Pn/cf7r+3eVyoa+vDyMjI2hubkZaWpooJAJqRqGphRYBL64JsABUaQZ8i2WeoJHkaZ8+TwxwEA6H5fzqNG/eK3+HBkpycrKQ5dpsNhw+fBjPPfccgsEgCgsLsXv3bjQ0NMhZ0y0PF3se/bpe0xttkNIB+yxADd8zm+fTdktKSuB2u3H58mXhP0hPT48xFGjoT01NScpuNBoVgJsReO4XZubwLASDwRjwjtlrmlODMpZzGwwGpXNbbm4uamtrMTc3h3feeQctLS3YuHGjAKJacV7L8xs/t0TW4ucO1BgNkqWcI+N+MH433ncoR42vGT+rdfJiQ4M88e5VB2jiPQcDH8bn/TIDNUYZE4lEUFpaCrvdjpdffhlJSUmoq6sT4vO+vj5YLBZkZWUhOzsbAKSLCDsgEiwlCOB0OlFYWChADNdIg6LA/BkkSXdVVRU8Hg/efvttvPDCC0hOTsaPf/xjVFdX4+mnn8bk5CR+/OMfi66n7I23z/7QuVlOoEYHMDgH+jf1b+sMG4fDgeLiYoRCIRw7dgxtbW0YGxvD9u3bhRskEAigrq4OK1euRFNTEyorK2PsCPIcMijFuedaBYNBFBUV4dFHH0U0GpXONHQEeb+6bJ78FtSb09PTmJychMlkQmZmJu69915UV1cjLS0NTqcTa9asQVNTE2ZnZ/HBBx/gt7/9Lbq7u1FXV4c777wTHR0d+Kd/+iekpqbiqaeewrZt20TOMzNoMSAmnrzRr3+eQA2H0WnVr2m5x240ZWVl6O/vx9tvv42RkRHk5+eLY9XQ0CBEzVNTUwiHw5L55vf7MTg4iLNnz0pbb7fbjUuXLuEb3/gGxsbGJOOC2SAulws9PT3o6urCxMSEdENl45KCggJpx52amorJyUkcPXoUzzzzDFwuFx577DHcf//9cDgcMc9sBO4XA+OvYyzLOs7MzPyU+sdo/y8lS4wBC4tlvs22w+EQDiCPxyMNYVjhkJWVhfXr16OoqAh+vx8ulyuGUJ2ADf0Ei2Wec4iZZFNTU0L2+uSTT6KyshL5+fno7u4WP5BykCVRFotFOu6VlJTggQcewI4dO1BVVSWZIxMTE+js7BSuVK/XK4TEtbW1WLVqlfi5i+lG41wZs3CNGYMJCQmfC1DD7M6CggLJEgsGg2hra8PBgwcxPT2N1atXS2t7zp/Wj4vtW40jaHBGAyn0Iwi8GHkwdRYMgCv+jheM4Pfi+T70ZQoKCmCz2fDJJ5/gX//1X3HhwgUUFRWhpaUFW7dula6l19PlaanA/h/MUaNJKDWCvZSDR6IhZsgQaWPUm6UP7Nzjcrmk21MgEMDY2BgKCwslAq8zGICFrgbs+kPkiwCDdioZKZ6bm5OyD5bvEBjQji43CaMZBHDS09PR3d0dE+XS87HUhrzR41p+V2/YjIwM3HnnnThw4ABee+01NDU1Yffu3UhISMDw8DAuXLgg0XQKHF32RcHqcrkwNjYmRGk0OjMzM8VBZ/QvEAjg4sWLGBsbQzQ6X3rm8XgwMDCAHTt2yB6JROaJSiORCMbHx1FeXo6srCzk5OQgKysLVVVVcDgcgnjygOvf6e7uRnt7O0ZHR1FRUYGamhpMT0/j0KFD6OnpwaOPPiqOo8lkipkbY7YY5zeeQ7RYBsxnXUOjIQIg5lxpcMZ4f/p+CHxt27YNFy9eRFtbG37729/iO9/5jrQ9pwPANqG6nlNHy6kQdZq2sU0h95fmYKCAZ1cggna8Pxq7eq55vVAohKysLFHoQ0ND+I//+A94vV4kJSWhtLQUlZWVQkyur8FzaETz4zkmy+BU3pTBs8RW3F/5ylfwzjvv4OWXX8bOnTtRWloKm82GtrY2+Hw+mEwmkYEsIyWPFxWL7nZHec211WdpbGwM1dXVMYAPFSbPH52BkZERZGVlob6+Hi6XC2+99RY2btyI9evXC0ijDZXlPD+30ojnXMR7z/j+YlkO/Jw2iOMFTYzXArAoQBjPYDQa1Iw0cs/o39URQS07teH+ZRtGgIwyxW6346677oLVasXzzz8Pr9eLrVu3oq6uDhcvXsSRI0eQmJiIdevWobq6Wq4xPj4u7aB5PnNzc1FXVydZbezQxmxXygKLxQKbzSaReq/Xi4MHD+L3v/891q9fjyeeeAIejwf/7//9P3i9XvzVX/0V6uvr4xq7t/KIt7evZSQmJqK6uhrf+973YLfb8eyzz+KNN96Ay+XCk08+iYSEBBw+fBiHDx9GfX09qqqqpIFCWVmZ6Kfe3l6xR5jVSweQjtuFCxeQkZGB3t5eNDY2orGxEUNDQ7BarWJvhkIhAexqampw/vx5TExMCB+NxWIRDg+bzYY1a9YIn83Q0BBefPFFnD9/HhkZGbj77rtRVFSEF154Aa+//jrWrl2LP//zP0dTU5MQWsbTbYuBxsACcemtOLgHdIBMZ+2aTCbhQGlpaUFmZiZ+97vf4cCBA3C5XNi9eze2b98u7ct7e3vR09MDt9stgJbX68VHH32E6elppKenSxYUI+gffvihBLd8Ph+GhoaQm5uLoqIirF+/Hg6HQ4DW9PR0FBQUSHZUf38/zp49i/b2dpw6dQqRSARPPvkk7rvvPiEq5bNQ/3IY/ZLPexjPITOXFwsiaGdbf8ZisQgQSTtzaGgIExMTOHLkiJyl+vp6bNq0CXV1dRgYGMDx48cxPDyM0tJSvPbaa7BarQgGg5I9XFRUJLaNyWRCaWkpHnnkEZGR5DQ6cOAADh06BIfDgWAwiIyMDAwPDyM7OxtOpxOPPPKIdLll2aPH44Hb7UZvby9GRkYku8fhcEj7bc2/xzXTmVY644i+p54vo516K6y72TxfBrR582ahLklJScHly5fx7rvvIhAI4Ac/+IEAF4mJiejr6xMbNB6wCiwkNHBfcO/Hq9qJl8nF/xt9p3j+EBDro/Bc6az7hIQE5OTkIC8vTxqX/Pu//ztmZ2el++qOHTvQ1NQkTW+udRiDDMbxBwE1FN46W0T/sEalNMqanJyM3NxcMUBSUlIEpdSLlZSUhNzc3BiiPafTCZ/PB7fbHZNSRMZufW8EhdgxiFkzdN60AakXnxk10eg834LD4YhpccvfJLI+MTEhtYck72MHHWbs6OwGo8F7Kwy9oSORCIqKivDggw+isrIS7733Hp555hm0tLRgzZo1CAQCOH/+PLq7u6UulxHA5ORkaVFZVVWFyclJDA4OSvlUKBRCR0cHXC4X6uvrkZGRIYfX4XAIQGc2m9He3o7e3l6MjY1hxYoV2LRpEyoqKgBAUo5ZmkOUmqAbBR+ZwonGj4+PS3vM1atXw263o62tDUeOHIHdbsdjjz2GhoYGARCvdoCA+EbMjTB0qdgWc+Y0iKiVo/4+78tms6Gqqgrbt2/H66+/jrfeegsOh0PSbJ1Op3T80ucqEpknF9XlTtpJpEDVkTpy0GjwhueVz8NMGoJ3WqYwGsHrsdQtIyMD4XAYv/vd73Dx4kVYLBYUFRVhxYoVQv7M68Q7c7o8RM+Rcb5vllOpDZl47+n7Mn5HZ0ulp6dj7969OHToEJ577jls3boVDQ0NWLduHU6dOoX+/n7ZI5RxLFOkcpyamoLH44HFMt9Za9WqVQgEAhgYGEBKSopEHZl9RUAvEonEZCUmJiZienoaubm5aGlpQVZWFo4ePYqTJ09ix44dWLt2rZR1LAYWLDUXX7ShdY7xdWN0TQ+jHuVreui9OjMzg7GxMdhsNiHhX8wgutow3hfvIR6hn/E7+t4pk6kTP++xlA5eCky7lqEBYQZ+du3ahaysLDz33HMYGRnBXXfdhcbGRuTm5uL8+fPw+XwSvZ2ZmUFeXh7S09MlMzghIQEZGRkiJ61Wq2TWaDkHLJQdcqSlpaGiogI//OEPsWLFChw/fhy/+c1vkJSUhL/9279FfX19zBpdyzm8FQZl2LXaVBrstFqtqK6uFoLS//zP/8TRo0cxMDCAJ554Ak899RTef/99vPrqqwiFQhKwWrNmDcrKyhAMBmE2m3Hs2DFMTk4K2MIs7oqKCtx5550oKysTp5ByNycnRzIlEhMTMTExAbvdjry8PCmBa29vh8fjgdlsRnZ2Nurr6zE2Noa0tDQpT3/nnXfQ0dEBk8mEbdu2YcuWLbh06RJ+8YtfwOPx4P7778dTTz2F0tLSmIj09YClPL/xgkC3wuDzsNQ93n4wm82SSdTU1ITMzEzU1dXhnXfewfHjx+F2u7F27Vo0NzejsrISaWlpAriR523dunWYnJzEhQsXxBH/1re+BYfDga1bt+L06dPIy8sTToqysjIEAgF0dnZiaGgIaWlpWL16NUpKSpCYmIjh4WGcPn0aJ06ckHbUzNLYvHkzMjMzr+h6qNdMZxncCoNnS4MzxsF9pNdIP5smfgXmswLXrVsnlBjs9DswMACv1wuXy4WVK1ciLy8PVqsV6enpIsdSU1Px4YcfyuspKSnYsGEDTp8+jdnZWVRVVaG5uRnBYBCHDx9GdnY2cnNz4XQ6sX37dkQiEXR1dYm+2rhxI5xOJ1asWIGSkhKkpKQgJSUF4+Pj8Hg8GBwchN/vx/j4OAKBAEwmE/Lz87Fq1SqUf5o1Fc+G13OhSzh1kMVY2qmTAW6FPWAymcQPy8rKQl5eHs6dO4fu7m5cvHgRf//3f48f/ehHWL9+PUpLS5GSkoL+/n4hWDb6n5qfRtuqQKydo+dRV8fobH5eR58jXkP7GVo3aN4j+jxFRUXIz89HJBLBRx99hJ///OfSya+5uVkyq1gBsKzzexUjZMk3dXnEYgYbsHA4tTFO7oTh4WGpP8zLy0NRUZEYI9HoPMHX6OioOI02mw1jY2Pw+XzCqs2InnYqOfkEU5hyxdQsgjWM5DNtkd2jGD2em5tDfn4+cnJypCyHz8QsnHA4jNHRUXg8HpSWlqK5uRl2u12QfD0H3HTXUP60bNpwbm7uqpamdiD4/BS6IyMjOHDgAC5evIhVq1ZJlyC/34+xsTFMTU0hOzsbRUVF0mLdZDLJ3M7NzUn5xNjYGLq6uuByuaS1WXl5ufCmWK1Wqf9+7bXX8N5778FsNmPr1q246667kJeXF1MCw1pr3jf5N0j05/V6MTQ0hP7+fkQiETQ0NAjvzcjICD744AMEAgFs2bIFO3bsEAQdgGR2GZVOPKBER3O00ElMTFyWdZydnY0awU/gysg496VRaGnlQDAlHA6jo6MDr7zyCg4dOgSz2Yw9e/bgT//0T4Xbpb+/XxwIzgWRbJLKcr51pIB7nHvI4XBgcnLyCkeAZywajUp6qQZYNOcCIwx5eXkC8L3xxhv4l3/5FyEHX7FiBR5++GE0NTXF8CQZQQDOmV4v45pScS7XGvLSxt/hnr3WsZgTSQOJ8+T3+6X9bn19PZqbmxGNRuHxeHDp0iV0dnbKGTWb57uyGc+91+tFVlYWHn74YQSDQezbty/GsMrJyYHP55OsKqa/siZft6Cdnp7GwYMH4XK5cPvtt6O6ujoGyDaWgC06gUvoLBoMi3x/udbxMyFFGmjRz2AEO41RH/15yprFHC0d7BgfHxdeLx2R0459vAiTcfAzWq5pMNz4jMaIn/G6xvr06xzLdha9Xu81reNnBQZ11JjrZrFY0N/fj4MHD+LYsWPYvHkztmzZIjKK3HmUfeTXY+ayLgvXMjQYDEoZsA4K6azHyclJjI2N4f3338e+ffuwdu1afPOb30RZWVncPamf4w8der96vV4UFxcvyzpG1Y1e633yWTXPBzOO/uu//gv79++HyWTCzp078Sd/8idISEjA73//e6Snp+Puu++O6V45NzeHkZERnD17Fr29vdLBx263Y+PGjaioqIDP55N22gCQm5sbY2NFo/Ptvk2m+WyLixcv4ty5c+jt7cXMzIx02SwqKkI4HMbp06exf/9+dHZ2IhAIYOPGjbj33nsBAPv378eRI0fgdDrxne98B1/72teEQwK4PpDUKIPigLE3TC9+pgtEF8hYjfYY5R2DCpFIBIODg7h8+TJaW1tx/PhxhEIhrFu3Drt27UJ9fT1MJpO0iO7q6sLIyAh6e3uRm5uLxsZGAMClS5cwOTmJ8k8zuy0WC4aGhjAyMiIdZkOhEFatWoXm5mYUFhais7MTHR0dGB4extjYGHp6epCWloZt27Zh/fr1kg2s9YHxfOpnWoaxLOsYCoXkOBr1ymL2Md/n67QbOLSMGxoawsWLF6WbFrtaZmZmora2FlVVVcjIyJBAYjgchsfjwejoKAKBAEpLS5GcnCy8p+wixGA7S3OAhaoM+iXUo7wndnoaGxvD6OiodHojP5HJZEJhYSFWrVqFgoICKb3Xel4HCuMFYIw2Pt/T3+X/k5KSbomzyOcYHx/HyZMnceLECQFskpKScO+992Lv3r3IyMhAIBCQAD5Bb/omrH4BIB2E6ffNzMwIPynngSAsEybinRe9l4xnJ55Nwwxzu92OgoICZGdnw+/348UXX8SLL76IzMxMrFy5Ehs2bMCmTZuQn5+/HEGo+IbrHwLU0MGgw2yM2hn/zUXga0REL1++jGAwiIKCAuTn50vJEg0QRneZ3k+Um3WnNESpeEOhEJKSkoRlnRvb5/NJeRNJ+4h+paSkSDs+1n6zs1RmZqZwLlCQ0AGm8TMyMoJwOIy8vDxs2LBBnIV4TkM0Gr0W1O2mAjUcxqga/x2JRHDx4kXs27cPfr8fDQ0NaGg/Sb4IAAAgAElEQVRoQH5+PkKhEPr6+jA2NiZksoWFhULsrLss8bo8FHTqWdqkW0BHo1EhY2OJhk4RBCA1qJOTk5iYmJCD7/f7xQDKzc2VAxcOhzE+Po5Tp07h0qVL2LhxI26//XZkZ2fHoLaam0a/DsQvK4o3j59eZ1mBGn19bZAv5uBq9JnCSzsBPp8Px48fx0svvYS2tjZYLBa0tLTgqaeeQlFREaampjAyMoKJiQlRKhSYZvMCYTcjlMzO4Jmn4OI58Pv9opBprPIs0BkhpxQdEt3OMj09Xbq3nT17Fv/4j/+IgYEBmM1m1NXV4e6770Zzc7OwsbM80SjntMOpgWS91nyGhISELwRQA1xZezszM4PBwUHs27cPXq8Xu3fvRmlpqWS+XLhwAUNDQ8KDwO8yEyYcDmPFihW45557EAqFcPjwYbS3t8Pn80kG2/j4OOx2uziRwHzWU21tLcrLyxEMBnH69Gl0dXVJe1QSZRoNFb2Pr3a2FpubWxGoMT4fsACu6teuBtQAC0akBkS0kUGZagQiNWjA1/Xf+l41GGT8nL4f/q7RmDQCNdpRvxoQd5XxhQBq4ukI2g8McB07dgyvvPIKUlNTsXPnTlRWVl7RQY1nWEepub5cT2bckBuPAQud5Tk2NoajR4/izJkzSEtLE3L9a+lG8WUDatR3Y5x6dqt77rnn8N///d/o6elBTk4Obr/9duzduxclJSWorq4WQNtsni8N5vxPT09jenpa7OHU1FR0dXWhvb0doVBIgHKfzye2CYmbgYX2sRaLRbK0yd83ODiIEydOoLW1FefOncPMzAyqqqrwyCOPoKGhAR9++CFeeuklzM3N4Z577sFDDz2ExsZG4XbkuN50/Kt95LomfOmxbGmSuoQBuDKDiDo/GAyKT3D8+HHs378fZ8+ehd1uR11dHXbt2oWmpiZkZWVJ59DR0VH09/ejt7cXbrc7plNiWlqa2L6lpaUoLi5GXl4e8vLyAEA4S9rb2zE8PCyZditWrEBzczMaGxulZB9Y0AXGcqdl4KQxjmW50PT0dFRnbWn9psG+ePetdYpRl2jdSaLaCxcuYHBwEOPj4wAAv98Pu90uXJnFxcVi09Op56B89fv9AsRMT08jNTVVWnszI0fTJtDv9Hq90sWJHSxJVBsIBJCTk4OCggI0NDRIZ1wGT7UvzL+NSQx6DowVK/rzOjkiOTn5ljiLvJ9IZJ7LsLe3Fx9//DHa2trQ0dGBwcFBVFZW4rHHHpOyNvrPbMlu1J18jfNHP4GZ2NFoVNbY7/dfUcqkzz71os441TaWTvSw2WwoLCxETk4OUlJS0NnZiWeeeQZHjhxBeXk5Vq9ejS1btqC5uVkwi2UYywvU0IAAFkot9OYDFso/+LdGASORea4Rj8eDrq4umEzzrRNzcnKEqwaAGClMtedE04mw2+1wOp2iNE2meWZ9EtyyViwhIUEOVyAQEGJLskHT6QyFQpiengYwj6gx/RhYcN55/xaLBX6/H/39/QgEAhgfH0dhYSE2btwoDosxPZwbQpdyXM+CfZaxGFCzmPCkYNL/p2DweDw4c+YMDh48iKmpKWzcuBHNzc3Izs4Wlvvh4WGEw2H09PQgHA6jpKRE+GvYHYsEpNwTFD6hUCiGuJkoNdeERtXo6CjGx8cxMTEhaZBEVp1OJ2pra1FRUYH09HSYTCbZZ4xUlZeXY+/evaiurhYOI20gA7GOTLxzouctXnQZwLI5+ZFIJKrPTjwgVN9nPOfL+BxEp0dHR/HBBx9g//79cLvdMJlMqK+vx/e+9z0UFRUhGAxiaGgoxgCis0jAB4CUMAKx3a8+BTuQnJyM8fHxmDbfTCskUDc7OyulcIxoUJmSE8lkMuH8+fP453/+Z3R1dUkp5a5du7Bz507hr6IxQ2BKG2ycRw38GZUfx3KBbVwK47pwPv/gCy/igGtAjpHhvXv3Snaa2+0WJcrOFlyH3Nxc7Ny5EwUFBUhISEB3d7eA6wTpCM6xg01ubq7M/+nTp3Hu3DmUl5dj+/btKCkpkfuNN9f6/0sBUosNnSUZZ9x0oEYrf2OkkPtNgx3G+nUjSMPsRG18ksSZnfD0HtdGSDyZAMSPfjKqBUAMJA1gchCk1aXDvKbx72VyLr4QQI0e2mnR6f3R6Hx221tvvYVz586huroamzZtktRwYH7uNa+XlmnajgqFQsjOzhY5x8wpAjSHDx+Gz+fDnj17sHfv3piyCu0E8H6Xe9yqQA2wYNDTBvB4PDh69Chef/11vPnmm3C5XLDb7di5cyceeughtLS0IC0tTcrbgflz4Ha7haeLbbvD4TDOnz+PcDiMXbt2ISMjA9FoVJx8ltVEo1Gkp6djdnYWAwMDGBwcxMTEBHw+H0ZGRnD06FGMjo7CarWioqICt99+O8rLy9HZ2YnXX38d09PT2Lp1Kx566CGsX79eyscBxAQf/jcANQBi+NS494yZxdrmn5qaQk9PD44fP45Dhw6hra1NbI6WlhY0NTWhvr4eDocDdrsdXq9X7B/KQJb+88ySgJikwgMDAxgfH8f09DSqqqqwcuVK1NfXo7CwEBkZGdJVkXoRWJDHPKdaBizjWJaLTU1NRXl/8XRFPPlitEsp54w6yWi7Tk5O4vLly7h48SImJiYwOTkpPiEpJxISEpCdnY2cnBwJ5tO3IDeizWYTn2N2dlYqIbhPaM/Ozs7C5XKJP0qid14rNTVVeDtLSkqEC4r3TDuWsl/rSa6ttuE1OGXUrZwTXvtTXtdl2xD0NajXjeugh14bZsfTduf9ezwenDx5EmfOnEFvby/6+/sRCoWwZs0a7NmzB8XFxUhMTBS5xz2g/Rx9jnld+u2cz+np6Ss+Y7Q7dKMMo83DZ2ZDHSYbTE1N4f3338cLL7wAAKirq0NFRQU2bdqEhoYGSfKg/7hY2d81juUHamhARCKRmL7leoKMRoDeYGS0Hx0dBTDv6KWmpsZEZukQBgIB6VTC32F0nuQ+Gt0mv43D4ZD7C4VCGB0dxeTkZMznuOhut1scTzqQGqgx1osSSBgaGsL09DR8Ph+cTifWrl0rz65TuIzpbldB4Jb14Blfu5oBbxSwFDA8NDQAT506hcnJSZSVlaGxsREVFRVSY+9yuTA4OChlbtPT05iZmZF2ykQ2OSc6PZh8GSSsBOZ5aerq6iSVkW1KCwsLheAtKytLDKTp6WmcP38enZ2dCIfDQtRXVVWFwsJCAdE0cGCs49ZzYBxLATW83nIh3XNzc1G9VhqBX0yhaWNef4bf12DjwMAA9u/fjw8++AAejwdJSUlobm7G448/jsrKSmHdn5qakufWApUt70wmk4BqBGAotO12OyYmJgTE0eeMDP10tHkmOc8OhwM5OTnSVvyf//mf0dnZKSBNc3Mz7rzzThQVFUkUw2gwGIE3o+Gg5087Rmaz+YYapDoz8XrHtYIZfKaBgQGcOnVKiBHXrVsnLSX9fj8CgYB0A+H5MJvNkurL9aZM5VparVaJ6A8PD+Pw4cPo7u7GmjVr0NLSgurqagHFFwM1l1JuX1Sght1fKM94dqg7AQhQR36vxYAaymFmjGpdy+wxDbRoHbxYFBO4EqiJRqOScapbrMf7fjAYFAP4/4CapUc82U1nsaOjA6+99hr6+/uxYcMGbNiwASUlJTHZhpRpRr1DmczzOjMzg5GREfT09ODjjz/GyZMnsXLlSjzyyCOoqKhYNABhNIaXc9wooAbX6eAbn8v4/Dw/oVAIAwMDOHHiBF555RX8/ve/h9frRVpamvB9NTc3o7a2FoWFhbBarQiHwzFd7xhUmJqaQldXF5xOp2RXAJAOQ36/H263G9FoFGfPnsW+ffvQ2toqhOzp6ekxXBd+vx8nTpzAqVOnYLfbsXv3bvzxH/8xamtrpaRDOx16LHX+PsPZvGWBGspGY1ahDmpRDvO1UCiEyclJDA8P45NPPsHx48dx6tQpDA0NSYZ+bm4uHnjgAZw+fVoCxVNTU9Lx1efzAZj3bUj0nZaWBpvNJrbKqlWrhLiY5Ku8Rw5th+jX/kAncLGxbBk1RkBBfsBkusK+4v91AEODUkBsBgmvw8GGBv39/ejv78fw8HCMb6eDGkY7kL9jtVpj/FTqTZ1pSH1rs9mEgoPv22w25OfnIzs7G3l5ebDZbDEZwnoNdZa+9gn1c+r54v7V2d4cvDa/k5KSsmybIhwORzVApINHxv1ozNbXNgXvj3ZCb2+vVDK43W6MjIwgJSUFmzdvRktLC9LT0zE2NgaPxyN2qM6w1vPG1wm8paSkwGazSXBCn2ujzRWNRmPwCo7ExETp6EQesE8++QQHDhxAX18fcnJyUF5ejqqqKqxevRplZWWSSTU1NSX3ooNo2sdeDOQyjOUFaghaGIGaeEiWvln979nZWXi9XhFuNDaIyJlMJgFq2DHI4/FIjanP50MkMk98S24EGpgWiwUFBQVITU2N2TjcCETAiHKmpKRIVH9ychIzMzNISkpCTk6OlD0RpadRmpCQgHA4LBw7JDuuq6uLEaiLCa6bBdREDYtsPFRxPh//hj5FmKPRqAg/l8uFtrY2HDt2DKOjo0hLS0NeXh7q6upQVlYm5UY8PHQCWV86OTkpjkxmZib6+/vF6Q8GgxgbG5NWaKmpqairqxNnn/dAJJ01jyT3CgQCKCkpQW1tLWpqapCdnS2HiczvFKxLATVLnZF4QI0+fMuVjRGJRKKM+PH3KAQpTLUy08JBKynj4Hn1er3o6urC22+/jcOHDyMUCiEajaK5uRlPPPEEysvLEQgEhJOJjO2aA4eCnR1KdJZKYmIi0tLS4HK5rogMMAKl5kyiIhkZGWLoAMDp06fxm9/8BqdPn4bFYkF+fj62bNmC2267DcXFxcjMzJT0ceNzGh0co8OilZFOwbyRHDXAzQFq9PqQXPvkyZP44IMP0N/fD7vdLnxRTqdTOIp4Thj5ZYcDk8kkHRWY1TY0NASXy4Xi4mKsX78eW7ZsiXE29Z692rMYxxcVqGFk1WKxCPl9NBoVxQ5AMkE1R9NiQA0zKEgmy8gh15eg6eTkpBiUNpstBoyWh1BzajS4yD3HNHzjPuM5IVCjW6vrz/8fULMw4skeHUGenJzEhx9+iN/97neIRCJoaWlBRUUFioqKJJKvU+jphHMdJicn0dfXh7a2Npw9exbDw8MoLy/HXXfdhc2bN4sMNWb46uf83wzUaP3KIILf78fly5dx9OhRvPvuuzh79ixGRkYwNTUlzntZWRnWrFmDtWvXoqKiAgUFBbBardLAwmw24/jx4/B6vaipqUF+fn5MZhRlaE9PD95//310d3cjLS0NGRkZYnu63W60t7dLF7DKykp89atfxbZt26TTJc+/7pZyPRk0/M71fPy6Lr70WNYNp51ko/OoP8PXqNNohwSDQfT392NsbAxDQ0Oi3/x+P6xWK0ZHR6VxBa+dmJgIl8sFAFL2b7PZkJGRgfLycpSWlqK8vBxFRUXSEZH3wYCHBpi0k6l1wq0M1BgddOMw2qKL8bPoYcyEou+mP8u22KQ/IOlwIBBANLrQYZZnglUZzHThWrKMiRk2JpNJgifscMvuP4WFhTH2pn5mbUtqv1dnuZlMJglkck603jfa7kZgRP/OcpY+TU1NRRn4YaY354nBVP0MemjbIR7dRSAQwIULF9De3o6RkRGMjY3B7XbD4XCgsbFRiKHpkxDI1rgCsNCgJBqNio3DTog9PT0x2crG/ajPFommWYZqsVjgcrlw6tQpnDx5EqOjo8jKykJhYSHy8vKwatUq1NTUSEkpfeKpqSnpgElswLju8WRxHLBmeYEazTXDGzECMfIjConTwoZdmAKBQAzAogkm9Xej0SgmJyfh8/mEv4bgiNVqlWgjN35RUZGQcnHQmefGASDZLXTgx8fHEQqFkJKSguzsbEG8+dy8HksymM7IDI+qqqqYEprFUouvgo7fsIwao7CMdw/GfaEPCOdOC9hAIIDe3l60t7fjwoUL8Hq9KC0tRUlJCbKzs5GVlQWz2Qyv1ysphna7HRkZGZidnRWhyg1NAUgBy5TGzs5OqUVkhpXH4xGwh+3zysrKUFpaiqysrBjHTStWALLuxufU/74aUKNRc6NSXa5sjEgkEo2XiWBUiPHOXjzjVL8eDocBzPMYnD17Fvv378f58+elq09tba2ANXTYWYbI86AN/HA4LIKd98J6T5fLhYmJiZgzrfkYOJfM0sjJyUF6ejpmZmZw5MgR/PrXv0Z3d7eU2mzbtg0tLS0oKCiA0+mMKZvTv2HkQdFCM94c8nOfyoYvPFDD90ksSqXLdqL9/f24dOkSBgcHhQ/M7/fD4/EAgDgAyjCAxWJBWloaUlNT4XQ6UVFRgfLycokUaqBa78v/TRk1QKzO4AgEAgAW5kKX+hmNUC2nKQ8pg5mpo/fs9PQ0vF4vpqamkJqaKiWnOsKsAel4oC6BWg16GmULAQLuDZKv6+tdo667nnHTgZprHVfbo0bwzahfuO6XL1/GwYMHceLECeHRcDgc2LFjB9ra2gAAWVlZmJqagtPplBbRo6OjCAaDsFqtqK2tlfp5kmAaHQmjzOP4sgI1RnsnXgBD/1sb+9PT0+jr60N3dzfa2trw0UcfoaurC4ODg/D5fHIW09PTUV5ejqamJmzatEm40mgrWiwWpKamIhKZ5xFKTU3FwMAAAoEAuru70dXVhZmZGQQCAQwODmJkZETs3Ly8PLS0tGD37t3Ytm0b0tPTr+DRABCT3WZc53iO1WLjaufVtLyIwfJuOMTqcePrOrPdqJd05jv9jJmZGWlOwS5d/CxtjoSEhJhmJCQGp52ruyoCC519eB98T3fDXCojchnHslxckwkbs2CMvpCxrCXmZgzn07g+WocZdRd1HIEbr9cr1RjsMqxtVWYg8v8Earh+zFCz2+1wOBxSUsj1IjjKTGGecQ6jLDWZYvnF+JxGwIr/1npaA1YaCPrU5ln2wL62DYx/L/HdRf1ejrm5OYyPj+Py5cvo6OiQCgmPx4OEhARUVVWhrq4O+fn5McT65ABjwoTf7xcuMHILWSwWCdwzYKznif5JcnIyCgoKpBSVmVkdHR04d+4cpqenhWuIHYyrqqqkqZBRj9LG0+v1GcfyATXa4NMLt9jNLWUckBNG15dpNIr/piPDevxodD6TIhAISAcYYN7ANJvNwlViNDTn5uak9pdRe12DzwUOh8NISUkRkiAeagoYfcjdbrccWqvViuLi4kVLaPSzL4aycW2WWpjrGeFwOGYdjb95LZsq3mEzGnfkD/L5fOjs7MSJEyfE8CDR7MDAAHJyclBWVobi4mIUFBQAmO9QcuHCBbhcLkQiETlEJpMJgUAAfr9fMqaYXsY0tby8PBQUFCAzM1OQTgrTePOvBd31RpwWmxutMPRYzowa497TCtwIFvE1nXVjdJa5RzUY5nK5cPz4cRw8eBCXLl0SZZGfn48f/vCHqKqqwtTUFPx+f4yjRgWnf0eXZRQUFCArKwvBYBCDg4OYmpqK4Y3hvdPJZsaUw+FAUlKStIgfGhqCyWRCQUEBtm3bhs2bNyM/Px/p6eki1HWphq4JNs7VYntDR0EikcgNZ9RfLqDmih9a4no6ks9sRW3wkH+Gn+Wc0iDh2dLyk5kd/G1taFzvWMx4u9pzmUy3JpkwsADYMLJEvcJoHtfE7XYjKysr7nPQADWZTEhLS4vp8kOQJikpCV6vF263GxaLRci3ObQDAOCKdaLhwiCH3hdGgIFdFJkabjTU+N3Pug/ijC8sUHOt16AMYqtXRvWZWQFAIo7AfJlFbm6upN/n5uZKGj4/eyPAl+sZNwuouRa5sRhQs9R3AUh3Q3Z7YUeg06dP49SpU+jt7YXL5RLbKDk5WUqXUlNTMTc3JxmiDO4BEN5E2k+MzJLvoqGhAWvXrsWqVatQVFQkkX1m61AWG5/vuifyOvfHjeRuW+6hwRodjTeZTDFl0kvtAcpoAiz8jpEWgN8hcM0RD6Qwfkfbd1fzq5ZxLMsPBIPBqAYU4u0noy4xvgfEysDFgjvx9roREDGuHf0H/l9XVGhblIOl2rqCYrEzwvvVlRrG+1nMRzDesy550oASn1GXTfH/NzqYeN0XuAqgA0Aqai5duiTk3Gz2Q5vC6XSiurpa7CHam2azWexW+p60R7n3IpGIdHzmWhBw5f4bHR3F+fPn0d3dLT4/AbmsrCyUl5ejuroaTqdzOTo6XctYPqBGk+HFXOwaHX69iMFgUEhoI5GI1Hdxw+qUK36eqCWdd3YcYemS1WpFWlqapDLx3njYZ2ZmhG+DKBzZu4mOzs3NwWq1SjaNdnr1oQ6Hw/D5fBKxdDqdEk3WaeBGYcyxRPnTsh28mZmZmIWK55wu9X8AVwhW4/+p/Ph9ooxutxuDg4Po6+uD3+9Hbm6u1MkzVTEQCCArKwuZmZkIBoO4fPkyLl26BL/fL2BZTk6OdHAi0k3nPF7tPu/RGI3QAE289dDfvZ5hBCz5/eUyZkgIbXSE+LtEnbUijDeMBgHPBUvSvF4v+vv78fHHH6O1tRUul0tKXoqKitDc3Iy7775bokok5ea9sGUp9wI7NaWlpQnp1uTkJEKhkGTSUQGyMxcwL8TT0tKQmJiIF198ES+99JJ0FauqqsK6deuwbds25OXlITU1FSkpKVcoUgpsY3THuB+0QWCcp08jpV8qoMaoRPX8xDMUdTYaFaARBF0sxXwpw2Sp8WUDanTUhc4e9RPnizooEAhIFNY4CMYw4qd1DImdbTYbpqenJVKlCdzpPAIQAD1eBk8wGBSjSBvJBHX5HUaQk5KSYLVaxWAyAsn/G4Aa4A8Ha4wlaNqp0QAyAMkyZTCKKfqaqP2znr/lHjcKqJmdnY2ZcF3eZwzExAMajc4U/15sHXVUn0GpQCAghOznz5+XlsAulwtjY2MIBoNC+KydQgYlye9VUlKCiooK5Ofno6KiAjU1NcjMzBQngXYxdb0xcr/Ufes50ONqcnWpvXOrdJq57h+KLmRe6CwaDr0PjLaC8RoEufVe0hkT8faXBmp4Lb7P128QF81iY1l+KBwOR+OBofGeL94+NNol12qX87s6U9oI+sQDf4ArKQs0EKK7hunf1zKa8tYIsmjwb7G5MF5PB8T4Gb3HOIx79VYEaq75h6JRsVuGh4fR3d0tIHYgEJDsfWDB9mD2GhMj6MPzb8691on8nXA4LFlWDDITGKedxHLW/Px8ZGZm3hJn8br7SV3NAbzad7WxoR1mbj6tjPR1uenJd8B/8zpk4QYg7OtaYGrhy7bb5EwhMkfuG15DOzAaJdYZOjRIuTl0mRTnyigsjHNyozdBPL4OPa71941KS1+HzjqwkHGVkpKC4uJilJSUYMOGDSLUvF4vnn/+ebz33ntC+puYmIiVK1fi61//Ou6//34h4WRmhY7o8xl0RoAWxABkXRZLG72aQfNZ1mSp+VmuEQ/008b8UvetDQV+F4g1LNLS0pCbm4sNGzYgHA7j6NGjGBkZwczMDM6ePYuBgQH09/fjgQceEBJFEriRh8Nsnu/AxhTFwcFBJCYmIjMzU5xVfp6EwwTcIpEI7HY7UlJSMDIygtdffx379+/H1NQU0tLSUFRUhHXr1mHHjh3Iy8uD3W6PS3hKxbmUo8ioilFBxzOuvkwjngwiL1C8yKJ2LADIOsVLbdZy9v9G7NBRHfLSMBBBIGdubk4I9Y0lejyjzJRg2rUGbnWJE/VbOBwWp95iscButyMajUrtt+6yyN8hMbhRvrANMQDpUsLf42/wu7zWjZCDX9ZBW0Ub/4s5jsnJyUhJSYlZAx0l1t+/FbJqbsQYGxuTf/M5dcYDn9/Ia6E53Yz8dBxGJ844aIOazfNcUEVFRdi6dSsCgYBknXq9Xgkusrx7YmJCSmJYUpGdnS3BRZ5/8koxOKjLJIEFrg7jvS61xp/V9os3CPh+0YYRCIkXhASuzMLV72ubwXgueT3NeaRfj5dRw3Or7/GLOLQNvpivc7VACxA/g0b7ixqc0e/xu/wMZZ72AY2lyEY/zbimlBUaOOH7lLNanujApb5XvhcPvIrno/EP5cBi++yLPOiPk8OytLQUfr9fQO7JyUkJ7JIcGlgAq2ZnZyXgq/1ADgbtCMgQ0GHrdoI9GRkZcDqdyMnJQUZGhtgwt8o5vG6gRmdNcKMaEcZ4YzFDnrXt2tDg+0aeF9YBR6Pz0UdymLD2kweEhMR6oikM6Tyy3Er/piaW0mnfRkfF+DqVqm6tpwEiLUCMzqAuQblRYzmU89XWVQNwixkPFJgWiwU7duzAypUr4fP5EAwGkZqaiqysLGRnZ8NkMsFut8NqtV4Bsun74W9rJyNeWZfx3pcCbj7rwbzRB1rvJ/3s2rEmMKUJfhf7jC590UrEbDYL18ttt90Gq9WKjo4OXLp0CSaTCT6fD62trbh06RJuu+02tLS0oLi4WIi1WdJEI87n8wmYYkwh5Rk3mUxCvsh20fv378eHH36IgYEBeDweOBwOrFq1CuvXr0dDQwOcTqfU5/OsL5YqawTw+BrnYTEyt5sxtJGgx3Lsp6WuYZTVxnNmBAL5b/1/oxMZ7zluxL3HM7L0e4w23ypKFlhwDnju4kXr+DpBD5PJFKNT5ubmMDQ0hOzsbMk809cYHx8XYny2Wddd1xiMACB8aiRQ1F2FqAeBWFBXc1LxHFMfM5uV90NZz+f63zKW2pvX832d9h7vfX326MQbjX/j95bjbC6lOz+P8fLLL8veBiAkoBw6mAPMZyGlpaXFzK8GnRcb/A3dWY26izYlARRtI/P88ZyRt4TZbNFoFD6fD5OTk3LvmtiUpVD6Hq/ShOKKPXijzuEPfvCDZb3ezR66/BNYoEaIZwsa9aEuTVkM0DMGm42gejxQ4Ms0uPfiyTEjuALEzpPRdzJmGhrtX/0dPZe6KYS2eY12oraJ9fX0vfP/uqKEAADlK+WE0c/j3xZA06IAAAd/SURBVIvdD59fy2na6xoQjAcqfhnAGv7NKpbs7GyRqz6fT4iFx8fHY2wo/mE1jPY/zeYFvkLOO0u0yd2Xnp4Om80mIM5igf3Pe1wXUBMPiDFuNC3k4m2seIgggRqtYI2OPQER7egx44LGJQ9IJBKRbiXGjJ3Z2VlMTExgampKDFIqPqJ2vA4Vr3bkdKo6BXViYqJkEhgRX+O8LDYHN3Jz3KhrL6bIOE9GgUIjJCkpCXa7HeXl5ZLOlpiYKNkVxppR4x/9m/EiI8a5vhUP3vUOI3u5MU1Sk9JpBUlj0Rg14Ptauek/JNmyWq2oqqrCqVOn0N7eDpfLBb/fj97eXoyOjuKjjz5CfX09GhoaUFZWhoyMDGFgN5vNyMrKigEBtIJlZgYjkH19fThx4gTOnDmDnp4eBINBBINBZGZmYv369di1a5f8hk551NEOXtsYzdLzpdPX9aBs4b+1Ur2RQxtsX4ZhnNeb6ah/EeZxMSccQIzzZ3zfbDYjIyMjJqtQf4ZR/NzcXAFLORc2m+0KkuVIZKFrk8lkiuG7Ma5ZJBKRFGV+1ph1atR7/J4OXnzZxmJ6fTmuZ7y2Udfpzxlf/0MBoy/KsNvt0o2HJUaagDIanS+pJa/M1NQU6urqBBQJhULSxWOpeYpEInLdoaEhBAIBJCUlYXx8HE6nE36/X0iCk5OTMT4+Lq18Tab5EkE2vJiamsLQ0BCqqqoQDoeRk5MjGcR0JiwWCwKBANatWxcDDhEg0mOp+9b67v/G0oNn7P+3dy47yS1BFC5RhOBWgjFeQuJEmaiMjQ70EZw49Ul5A0fqlESjjowxUSAiCvIP/qw+izoNwol6gKwvMYrivjR9qVpVuzoG2tEHYWPzHR/P+0f4jq9pFLFjcw8/kYDX3veJBa28DxWbb2P2PgcLONDuBQ/213hN9r4erpPrFLEQx8fj9Q5+Kf4X52Ghj4/P7cP35P/er72nBfatsakIAlhcHwp+JQqus9/jkyfgc/DjUZPSdiNn1Pgb63ej3qH2BgR3ZHaiWI00+0ck4SgpDEUeuIgWssKWTqfDM2o4DzJpWA3HwphOp63RaITrw2Bkhw21PDgdzczCudiY8pO+rwj9f4oIsfMOmgS+Mki9WBIDwhgmT/wOO9G0Wq2QHeUnvH7XGDtfTED6ilHfN4zx+52fLRueeM2iGKJ2Zr1p3X7ixzVxZMBnH0CwKBQKNjc3Z0tLS7aysmKlUsmq1apVq1V7enqy19fXsB36+fm5ZTKZsLVzLpcLWWnIssHYhMCCjDgco9FohLpTMLA3NzetXC6H7bexe4Jf5Hx78+SMe/TRCY56sCGB48Fp/kmwoPRz3CcR/5n85hwXE3THDR/JBUjDRd0nX7xudnY27ODjHQWsY8hswWtE/Hh98mAsdDqdIJ7y2OL5GkYSnFbus7xddMyJEaMRW9f6OTO+L/iI7rSSz+etUqnY1dVVz44uq6urdn9/b0mSWKlUCjvabW9vW6VSsWw2a8/Pz/b29mZnZ2c9G0CY/VvgwJhtNpt2cXFhtVot1DlcX1+3drtte3t7dnt7a91uN2xYsbi4aPV6PRR8TpLErq+vbWFhIezUdXR0FOr5welAYcvj4+Po+ix+hkHrBq8r7OB7/4bte+/LfLewO06wgGXWmzES67M+m2mQzdBPbMTahPHJ6xbmRZ4LvZDm50yeSzmgicdqYEey0IJj4zsH9rk9WCjy1xSrQeP7FQdl/fGmHfjwXFqE8YFo/M+0MJJQM4wB7DtWPwfeO1UoNMrF4Fi95mOiGjc6KSZAZNrAceVOjGwdPJKBa/n8/Ox5zjubzYZ0UxZeOO0UUQ9EOFCEiFVWf4/j2Gn+6+Ife29sgMQ+b0xcqKWAz4UH4DDtxufivjXIQB12kewnxny36DMKMWHBrHerQi9OsCPFfRh/Y0EnluY5MzMTtsaen5+3JElsY2PDtra27O7uzm5ubqxWq9nj42Mo/vXy8tJznZySDvEUCx8epYBowzWmisWiFYtF293dtZ2dHcvn81YoFILIw0YBIoyIWnB7cSSDF0WO7mCR9Pf/m4/PjLu4MClMQhty3wSYPzE+Yn2PDTWsd+jXnU4npPQi6vf+/h7mAw4ScHCBs2782GDjB8IP1keuD4f3ogg/z5uK5oufpNv9+xh8uVy2arVqp6enlkql7PLy0vb3961YLNrHx4cdHByE8ZAkibXbbctms2Eb1larFbJAY1FtZJSlUik7OTkJgcJarWbLy8shEHF4eBjGG0TTh4cHW1tbs0wmY81mM2QW4xy5XC7UWkDh/nq9Hh4FRoHuWEACbTAskzA/jiPwMwDbu6OULpjm9h9k9/v7jrUDrz34eyyr2dvC3tf0PgELJV7oZjEHP/MOmCzK8Pn7fZn1PuLmhXPfPvwzb/Ecs9txDs4il2j7l2kUZ5ivdn0SQgghhBBCCCGEEL/E9D0gKYQQQgghhBBCCDGhSKgRQgghhBBCCCGEGBMk1AghhBBCCCGEEEKMCRJqhBBCCCGEEEIIIcYECTVCCCGEEEIIIYQQY4KEGiGEEEIIIYQQQogx4Q+1YPSQJJRuoQAAAABJRU5ErkJggg=="
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "id": "3PYcSlVGTdRx",
        "outputId": "74044bee-ec7d-4de1-e735-31b770d051e8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ResNet "
      ],
      "metadata": {
        "id": "qWITu3WVRI6n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* ResNet16 Model"
      ],
      "metadata": {
        "id": "H4I1L6cOVLGH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Deep Residual Learning for Image Recognition** 에서의 **ResNet** 구현\n",
        "\n",
        "ResNet: Residual Network\n",
        "이전 Layer의 Feature Map을 다음 Layer의 Feature Map에 더해주는 Skip Connection 구조로 이루어져 있음 "
      ],
      "metadata": {
        "id": "O0IhBUW3R-vu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![resnet]()"
      ],
      "metadata": {
        "id": "RBSODlK0SEiG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "class BasicBlock(nn.Module):\r\n",
        "  def __init__(self, in_planes, planes, stride = 1):\r\n",
        "    super(BasicBlock, self).__init__()\r\n",
        "    self.conv1 = nn.Conv2d(in_planes, planes,\r\n",
        "                           kernel_size = 3,\r\n",
        "                           stride = stride,\r\n",
        "                           padding = 1,\r\n",
        "                           bias = False)\r\n",
        "    self.bn1 = nn.BatchNorm2d(planes)\r\n",
        "    self.conv2 = nn.Conv2d(planes, planes,\r\n",
        "                           kernel_size = 3,\r\n",
        "                           stride = 1,\r\n",
        "                           padding = 1,\r\n",
        "                           bias = False)\r\n",
        "    self.bn2 = nn.BatchNorm2d(planes)\r\n",
        "\r\n",
        "    self.shortcut = nn.Sequential()\r\n",
        "    if stride != 1 or in_planes != planes:\r\n",
        "      self.shortcut = nn.Sequential(\r\n",
        "          nn.Conv2d(in_planes, planes,\r\n",
        "                           kernel_size = 3,\r\n",
        "                           stride = stride,\r\n",
        "                           padding = 1,\r\n",
        "                           bias = False),\r\n",
        "          nn.BatchNorm2d(planes))\r\n",
        "      \r\n",
        "  def forward(self, x):\r\n",
        "    out = F.relu(self.bn1(self.conv1(x)))\r\n",
        "    out = self.bn2(self.conv2(out))\r\n",
        "    out += self.shortcut(x)\r\n",
        "    out = F.relu(out)\r\n",
        "    return out\r\n",
        "\r\n",
        "class ResNet(nn.Module):\r\n",
        "    def __init__(self, n_classes = 2): \r\n",
        "        super(ResNet, self).__init__() \r\n",
        "\r\n",
        "        self.in_planes = 16\r\n",
        "\r\n",
        "        self.conv1 = nn.Conv2d(1, 16,\r\n",
        "                                kernel_size = 3,\r\n",
        "                                stride = 1,\r\n",
        "                                padding = 1,\r\n",
        "                                bias = False)\r\n",
        "        self.bn1 = nn.BatchNorm2d(16)\r\n",
        "        self.layer1 = self._make_layer(BasicBlock, 16, 2, stride = 1)\r\n",
        "        self.layer2 = self._make_layer(BasicBlock, 32, 2, stride = 2)\r\n",
        "        self.layer3 = self._make_layer(BasicBlock, 64, 2, stride = 2)\r\n",
        "        self.layer4 = self._make_layer(BasicBlock, 128, 2, stride = 2)\r\n",
        "        self.linear = nn.Linear(1152, n_classes)\r\n",
        "\r\n",
        "    def _make_layer(self, block, planes, num_blocks, stride):\r\n",
        "        strides = [stride] + [1] * (num_blocks - 1)\r\n",
        "        layers = []\r\n",
        "        for stride in strides:\r\n",
        "          layers.append(block(self.in_planes, planes, stride))\r\n",
        "          self.in_planes = planes\r\n",
        "        return nn.Sequential(*layers)\r\n",
        "\r\n",
        "    def forward(self, x):\r\n",
        "        x = F.relu(self.bn1(self.conv1(x)))\r\n",
        "        x = self.layer1(x)\r\n",
        "        x = self.layer2(x)\r\n",
        "        x = self.layer3(x)\r\n",
        "        x = self.layer4(x)\r\n",
        "        x = F.avg_pool2d(x, 8)\r\n",
        "        x = x.view(x.size(0), -1)\r\n",
        "        x = self.linear(x)\r\n",
        "\r\n",
        "        return x\r\n",
        "\r\n",
        "resnet = ResNet().to(DEVICE)\r\n",
        "summary(resnet, X_train.shape[1:]) # summary code "
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1         [-1, 16, 227, 227]             144\n",
            "       BatchNorm2d-2         [-1, 16, 227, 227]              32\n",
            "            Conv2d-3         [-1, 16, 227, 227]           2,304\n",
            "       BatchNorm2d-4         [-1, 16, 227, 227]              32\n",
            "            Conv2d-5         [-1, 16, 227, 227]           2,304\n",
            "       BatchNorm2d-6         [-1, 16, 227, 227]              32\n",
            "        BasicBlock-7         [-1, 16, 227, 227]               0\n",
            "            Conv2d-8         [-1, 16, 227, 227]           2,304\n",
            "       BatchNorm2d-9         [-1, 16, 227, 227]              32\n",
            "           Conv2d-10         [-1, 16, 227, 227]           2,304\n",
            "      BatchNorm2d-11         [-1, 16, 227, 227]              32\n",
            "       BasicBlock-12         [-1, 16, 227, 227]               0\n",
            "           Conv2d-13         [-1, 32, 114, 114]           4,608\n",
            "      BatchNorm2d-14         [-1, 32, 114, 114]              64\n",
            "           Conv2d-15         [-1, 32, 114, 114]           9,216\n",
            "      BatchNorm2d-16         [-1, 32, 114, 114]              64\n",
            "           Conv2d-17         [-1, 32, 114, 114]           4,608\n",
            "      BatchNorm2d-18         [-1, 32, 114, 114]              64\n",
            "       BasicBlock-19         [-1, 32, 114, 114]               0\n",
            "           Conv2d-20         [-1, 32, 114, 114]           9,216\n",
            "      BatchNorm2d-21         [-1, 32, 114, 114]              64\n",
            "           Conv2d-22         [-1, 32, 114, 114]           9,216\n",
            "      BatchNorm2d-23         [-1, 32, 114, 114]              64\n",
            "       BasicBlock-24         [-1, 32, 114, 114]               0\n",
            "           Conv2d-25           [-1, 64, 57, 57]          18,432\n",
            "      BatchNorm2d-26           [-1, 64, 57, 57]             128\n",
            "           Conv2d-27           [-1, 64, 57, 57]          36,864\n",
            "      BatchNorm2d-28           [-1, 64, 57, 57]             128\n",
            "           Conv2d-29           [-1, 64, 57, 57]          18,432\n",
            "      BatchNorm2d-30           [-1, 64, 57, 57]             128\n",
            "       BasicBlock-31           [-1, 64, 57, 57]               0\n",
            "           Conv2d-32           [-1, 64, 57, 57]          36,864\n",
            "      BatchNorm2d-33           [-1, 64, 57, 57]             128\n",
            "           Conv2d-34           [-1, 64, 57, 57]          36,864\n",
            "      BatchNorm2d-35           [-1, 64, 57, 57]             128\n",
            "       BasicBlock-36           [-1, 64, 57, 57]               0\n",
            "           Conv2d-37          [-1, 128, 29, 29]          73,728\n",
            "      BatchNorm2d-38          [-1, 128, 29, 29]             256\n",
            "           Conv2d-39          [-1, 128, 29, 29]         147,456\n",
            "      BatchNorm2d-40          [-1, 128, 29, 29]             256\n",
            "           Conv2d-41          [-1, 128, 29, 29]          73,728\n",
            "      BatchNorm2d-42          [-1, 128, 29, 29]             256\n",
            "       BasicBlock-43          [-1, 128, 29, 29]               0\n",
            "           Conv2d-44          [-1, 128, 29, 29]         147,456\n",
            "      BatchNorm2d-45          [-1, 128, 29, 29]             256\n",
            "           Conv2d-46          [-1, 128, 29, 29]         147,456\n",
            "      BatchNorm2d-47          [-1, 128, 29, 29]             256\n",
            "       BasicBlock-48          [-1, 128, 29, 29]               0\n",
            "           Linear-49                    [-1, 2]           2,306\n",
            "================================================================\n",
            "Total params: 788,210\n",
            "Trainable params: 788,210\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.20\n",
            "Forward/backward pass size (MB): 142.45\n",
            "Params size (MB): 3.01\n",
            "Estimated Total Size (MB): 145.65\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M9yo9J_ORsdu",
        "outputId": "e063edeb-430d-4899-f180-7060779d60d0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "optimizer = optim.Adam(resnet.parameters(), lr=0.001)\r\n",
        "criterion = nn.CrossEntropyLoss()"
      ],
      "outputs": [],
      "metadata": {
        "id": "-Cv7O0o0Rsw1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Train Model"
      ],
      "metadata": {
        "id": "rooJ91V3U86Y"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "def train(model, train_loader, optimizer):\r\n",
        "    model.train()                         # 모델 train 상태로\r\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\r\n",
        "        data, target = data.to(DEVICE), target.to(DEVICE)  # data, target 값 DEVICE에 할당\r\n",
        "        optimizer.zero_grad()                              # optimizer gradient 값 초기화\r\n",
        "        output = model(data)                               # 할당된 데이터로 output 계산\r\n",
        "        loss =  criterion(output, target)                  # Cross Entropy Loss 사용해 loss 계산\r\n",
        "        loss.backward()                                    # 계산된 loss back propagation\r\n",
        "        optimizer.step()                                   # parameter update"
      ],
      "outputs": [],
      "metadata": {
        "id": "NBMOFujjU9iL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Evaluate Model"
      ],
      "metadata": {
        "id": "9RxzeapWVC8-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "def evaluate(model, test_loader):\r\n",
        "    model.eval()      # 모델 평가 상태로\r\n",
        "    test_loss = 0     # test_loss 초기화\r\n",
        "    correct = 0       # 맞게 예측한 0 값으로 초기화\r\n",
        "    \r\n",
        "    with torch.no_grad(): \r\n",
        "        for data, target in test_loader:\r\n",
        "            data, target = data.to(DEVICE), target.to(DEVICE)     # data, target DEVICE에 할당\r\n",
        "            output = model(data)                                  # output 계산\r\n",
        "            test_loss += criterion(output, target).item()         # loss 계산(총 loss 에 더해주기)\r\n",
        "            pred = output.max(1, keepdim=True)[1]                 # 계산된 벡터값 중 가장 큰 값 가지는 class 예측\r\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item() # 맞게 예측한 값 세기\r\n",
        "   \r\n",
        "    test_loss /= len(test_loader.dataset)                         # 평균 loss\r\n",
        "    test_accuracy = 100. * correct / len(test_loader.dataset)     # test(validation) 데이터 정확도\r\n",
        "    return test_loss, test_accuracy "
      ],
      "outputs": [],
      "metadata": {
        "id": "QzZpfMSrVDpF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train ResNet"
      ],
      "metadata": {
        "id": "LfbgiyabVnIW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "import time\r\n",
        "import copy\r\n",
        " \r\n",
        "def train_model(model ,train_loader, val_loader, optimizer, num_epochs = 30):\r\n",
        "    best_acc = 0.0  # beset accuracy 초기화\r\n",
        "    best_model_wts = copy.deepcopy(model.state_dict()) \r\n",
        " \r\n",
        "    for epoch in range(1, num_epochs + 1):\r\n",
        "        since = time.time()                                     # 학습 시간 계산\r\n",
        "        train(model, train_loader, optimizer)                   # train 데이터로 학습\r\n",
        "        train_loss, train_acc = evaluate(model, train_loader)   # train_loss, train_acc 계산\r\n",
        "        val_loss, val_acc = evaluate(model, val_loader)         # valid_loss, valid_acc 계산\r\n",
        "        \r\n",
        "        if val_acc>best_acc:  # update best accuracy\r\n",
        "            best_acc = val_acc\r\n",
        "            best_model_wts = copy.deepcopy(model.state_dict())\r\n",
        "        \r\n",
        "        time_elapsed = time.time() - since # 학습 시간 출력\r\n",
        "        print('-------------- epoch {} ----------------'.format(epoch))\r\n",
        "        print('train Loss: {:.4f}, Accuracy: {:.2f}%'.format(train_loss, train_acc))   \r\n",
        "        print('val Loss: {:.4f}, Accuracy: {:.2f}%'.format(val_loss, val_acc))\r\n",
        "        print('Completed in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60)) \r\n",
        "\r\n",
        "    model.load_state_dict(best_model_wts)  \r\n",
        "    return model\r\n",
        "\r\n",
        "model = train_model(resnet ,train_loader, test_loader, optimizer)  \t# 모델 학습시키기\r\n",
        "torch.save(model,'ResNet.pt') "
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------- epoch 1 ----------------\n",
            "train Loss: 0.0084, Accuracy: 77.03%\n",
            "val Loss: 0.0083, Accuracy: 76.33%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 2 ----------------\n",
            "train Loss: 0.0061, Accuracy: 80.27%\n",
            "val Loss: 0.0318, Accuracy: 64.71%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 3 ----------------\n",
            "train Loss: 0.0055, Accuracy: 85.97%\n",
            "val Loss: 0.0429, Accuracy: 64.50%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 4 ----------------\n",
            "train Loss: 0.0065, Accuracy: 80.10%\n",
            "val Loss: 0.0248, Accuracy: 66.67%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 5 ----------------\n",
            "train Loss: 0.0027, Accuracy: 93.26%\n",
            "val Loss: 0.0187, Accuracy: 66.88%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 6 ----------------\n",
            "train Loss: 0.0066, Accuracy: 83.94%\n",
            "val Loss: 0.0127, Accuracy: 71.77%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 7 ----------------\n",
            "train Loss: 0.0038, Accuracy: 90.19%\n",
            "val Loss: 0.0259, Accuracy: 66.12%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 8 ----------------\n",
            "train Loss: 0.0050, Accuracy: 88.12%\n",
            "val Loss: 0.0092, Accuracy: 83.28%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 9 ----------------\n",
            "train Loss: 0.0012, Accuracy: 97.34%\n",
            "val Loss: 0.0184, Accuracy: 77.74%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 10 ----------------\n",
            "train Loss: 0.0008, Accuracy: 98.34%\n",
            "val Loss: 0.0143, Accuracy: 77.09%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 11 ----------------\n",
            "train Loss: 0.0005, Accuracy: 98.97%\n",
            "val Loss: 0.0192, Accuracy: 76.76%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 12 ----------------\n",
            "train Loss: 0.0006, Accuracy: 98.51%\n",
            "val Loss: 0.0186, Accuracy: 75.68%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 13 ----------------\n",
            "train Loss: 0.0006, Accuracy: 98.72%\n",
            "val Loss: 0.0348, Accuracy: 76.87%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 14 ----------------\n",
            "train Loss: 0.0003, Accuracy: 99.57%\n",
            "val Loss: 0.0324, Accuracy: 72.64%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 15 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0331, Accuracy: 76.33%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 16 ----------------\n",
            "train Loss: 0.0001, Accuracy: 99.81%\n",
            "val Loss: 0.0260, Accuracy: 79.91%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 17 ----------------\n",
            "train Loss: 0.0001, Accuracy: 99.95%\n",
            "val Loss: 0.0419, Accuracy: 76.11%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 18 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0347, Accuracy: 76.44%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 19 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0361, Accuracy: 76.22%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 20 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0336, Accuracy: 76.66%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 21 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0329, Accuracy: 76.98%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 22 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0358, Accuracy: 76.87%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 23 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0333, Accuracy: 76.76%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 24 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0370, Accuracy: 76.66%\n",
            "Completed in 0m 53s\n",
            "-------------- epoch 25 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0361, Accuracy: 76.44%\n",
            "Completed in 0m 53s\n",
            "-------------- epoch 26 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0347, Accuracy: 76.66%\n",
            "Completed in 0m 52s\n",
            "-------------- epoch 27 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0348, Accuracy: 76.98%\n",
            "Completed in 0m 54s\n",
            "-------------- epoch 28 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0347, Accuracy: 76.66%\n",
            "Completed in 0m 53s\n",
            "-------------- epoch 29 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0329, Accuracy: 77.74%\n",
            "Completed in 0m 54s\n",
            "-------------- epoch 30 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0353, Accuracy: 76.98%\n",
            "Completed in 0m 54s\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rmk9kEN2VpbV",
        "outputId": "a0a4b0c8-79c3-4781-b81f-04826a5ecbde"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test ResNet"
      ],
      "metadata": {
        "id": "wDIf1rOTVtqw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "model=torch.load('ResNet.pt')\r\n",
        "model.eval()\r\n",
        "loss, acc = evaluate(model, test_loader)\r\n",
        "\r\n",
        "print('Test Accuracy: {:.4f}'.format(acc))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 83.2790\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vck5U8sCcPgR",
        "outputId": "06091aac-c110-41c3-bada-ee07517e71f7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "from sklearn.metrics import classification_report\r\n",
        "\r\n",
        "def prediction(model, data_loader):\r\n",
        "    model.eval()\r\n",
        "    predlist=torch.zeros(0,dtype=torch.long, device='cpu')\r\n",
        "    lbllist=torch.zeros(0,dtype=torch.long, device='cpu')\r\n",
        "    \r\n",
        "    with torch.no_grad():\r\n",
        "      for i, (data, label) in enumerate(data_loader):\r\n",
        "        data = data.to(DEVICE)        # 데이터 DEVICE에 할당\r\n",
        "        label = label.to(DEVICE)      # 라벨 값 DEVICE에 할당\r\n",
        "        outputs = model(data)         # 예측\r\n",
        "        _, preds = torch.max(outputs, 1)  # 가장 높은 확률 가지는 class 예측\r\n",
        "\r\n",
        "        # Batch 단위 예측값 append 하기\r\n",
        "        predlist=torch.cat([predlist,preds.view(-1).cpu()])\r\n",
        "        lbllist=torch.cat([lbllist,label.view(-1).cpu()])\r\n",
        "        \r\n",
        "    # Classification Report\r\n",
        "    print(classification_report(lbllist.numpy(), predlist.numpy())) # 클래스별 accuracy, recall, f1-score \r\n",
        "    return "
      ],
      "outputs": [],
      "metadata": {
        "id": "ox29vVfeW4gv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "prediction(model, test_loader)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.92      0.86       503\n",
            "           1       0.88      0.73      0.80       418\n",
            "\n",
            "    accuracy                           0.83       921\n",
            "   macro avg       0.84      0.82      0.83       921\n",
            "weighted avg       0.84      0.83      0.83       921\n",
            "\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Qzh5k18iL0E",
        "outputId": "7dfeecd7-7da3-4af7-9e04-5a64c43ea477"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [],
      "outputs": [],
      "metadata": {
        "id": "X4VhHCl6k6IQ"
      }
    }
  ]
}